Methods and systems for generating lifestyle change recommendations based on biological extractions

ABSTRACT

In an aspect, a system for generating lifestyle change recommendations based on biological extractions includes a computing device designed and configured for receiving a biological extraction pertaining to a user generating, using a first machine-learning process, a plurality of lifestyle intervention combinations as a function of the biological extraction, assigning, to each lifestyle intervention combination of the plurality of lifestyle intervention combinations, a degree of projected user adherence to the lifestyle intervention combination, wherein assigning further comprises performing a second machine learning process, and selecting, from the plurality of lifestyle intervention combinations, a lifestyle intervention combination as a function of the degree of projected user adherence of the selected lifestyle intervention combination.

FIELD OF THE INVENTION

The present invention generally relates to the field of artificialintelligence. In particular, the present invention is directed tomethods and systems for generating lifestyle change recommendationsbased on biological extractions.

BACKGROUND

Significant constitutional improvements can result from lifestylemodifications but identifying a maximally effective set thereof can be acomplex task fraught with uncertainty. This problem is compounded by thefact that effects can vary depending on underlying circumstances, suchthat an ideal solution in one situation may fall short in another.

SUMMARY OF THE DISCLOSURE

In an aspect, a system for generating lifestyle change recommendationsbased on biological extractions includes a computing device designed andconfigured for receiving a biological extraction pertaining to a usergenerating, using a first machine-learning process, a plurality oflifestyle intervention combinations as a function of the biologicalextraction, assigning, to each lifestyle intervention combination of theplurality of lifestyle intervention combinations, a degree of projecteduser adherence to the lifestyle intervention combination, whereinassigning further comprises performing a second machine learningprocess, and selecting, from the plurality of lifestyle interventioncombinations, a lifestyle intervention combination as a function of thedegree of projected user adherence of the selected lifestyleintervention combination.

In another aspect, a method of generating lifestyle changerecommendations based on biological extractions includes receiving, by acomputing device, a biological extraction pertaining to a user,generating, by the computing device, and using a first machine-learningprocess, a plurality of lifestyle intervention combinations as afunction of the biological extraction, assigning, by a computing deviceand to each lifestyle intervention combination of the plurality oflifestyle intervention combinations, a degree of projected useradherence to the lifestyle intervention combination, wherein assigningfurther comprises performing a second machine learning process, andselecting, from the plurality of lifestyle intervention combinations, alifestyle intervention combination as a function of the degree ofprojected user adherence to the selected lifestyle interventioncombination.

These and other aspects and features of non-limiting embodiments of thepresent invention will become apparent to those skilled in the art uponreview of the following description of specific non-limiting embodimentsof the invention in conjunction with the accompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

For the purpose of illustrating the invention, the drawings show aspectsof one or more embodiments of the invention. However, it should beunderstood that the present invention is not limited to the precisearrangements and instrumentalities shown in the drawings, wherein:

FIG. 1 is a block diagram of an exemplary embodiment of a system forgenerating lifestyle change recommendations based on biologicalextractions;

FIG. 2 is a block diagram of an exemplary embodiment of a user database;

FIG. 3 is a block diagram of an exemplary embodiment of an interventiondatabase;

FIG. 4 is a block diagram of an exemplary embodiment of an expertdatabase;

FIG. 5 is a flow diagram of an exemplary embodiment of a methodgenerating lifestyle change recommendations based on biologicalextractions; and

FIG. 6 is a block diagram of a computing system that can be used toimplement any one or more of the methodologies disclosed herein and anyone or more portions thereof.

The drawings are not necessarily to scale and may be illustrated byphantom lines, diagrammatic representations and fragmentary views. Incertain instances, details that are not necessary for an understandingof the embodiments or that render other details difficult to perceivemay have been omitted.

DETAILED DESCRIPTION

Embodiments disclosed herein generate lifestyle interventions, andcombinations thereof, that are potentially beneficial given a user'sbiological extraction, using one or more machine-learning processes.Projections of likely user adherence to such intervention combinationsmay be determined using further machine-learning or classificationprocesses. Combinations may be filtered according to user restrictions.

Referring now to FIG. 1 , an exemplary embodiment of a system 100 forgenerating lifestyle change recommendations based on biologicalextractions is illustrated. System 100 includes a computing device 104.Computing device 104 may include any computing device as described inthis disclosure, including without limitation a microcontroller,microprocessor, digital signal processor (DSP) and/or system on a chip(SoC) as described in this disclosure. Computing device may include, beincluded in, and/or communicate with a mobile device such as a mobiletelephone or smartphone. Computing device 104 may include a singlecomputing device operating independently, or may include two or morecomputing device operating in concert, in parallel, sequentially or thelike; two or more computing devices may be included together in a singlecomputing device or in two or more computing devices. Computing device104 may interface or communicate with one or more additional devices asdescribed below in further detail via a network interface device.Network interface device may be utilized for connecting computing device104 to one or more of a variety of networks, and one or more devices.Examples of a network interface device include, but are not limited to,a network interface card (e.g., a mobile network interface card, a LANcard), a modem, and any combination thereof. Examples of a networkinclude, but are not limited to, a wide area network (e.g., theInternet, an enterprise network), a local area network (e.g., a networkassociated with an office, a building, a campus or other relativelysmall geographic space), a telephone network, a data network associatedwith a telephone/voice provider (e.g., a mobile communications providerdata and/or voice network), a direct connection between two computingdevices, and any combinations thereof. A network may employ a wiredand/or a wireless mode of communication. In general, any networktopology may be used. Information (e.g., data, software etc.) may becommunicated to and/or from a computer and/or a computing device.Computing device 104 may include but is not limited to, for example, acomputing device or cluster of computing devices in a first location anda second computing device or cluster of computing devices in a secondlocation. Computing device 104 may include one or more computing devicesdedicated to data storage, security, distribution of traffic for loadbalancing, and the like. Computing device 104 may distribute one or morecomputing tasks as described below across a plurality of computingdevices of computing device, which may operate in parallel, in series,redundantly, or in any other manner used for distribution of tasks ormemory between computing devices. Computing device 104 may beimplemented using a “shared nothing” architecture in which data iscached at the worker, in an embodiment, this may enable scalability ofsystem 100 and/or computing device.

Computing device 104 may be designed and/or configured to perform anymethod, method step, or sequence of method steps in any embodimentdescribed in this disclosure, in any order and with any degree ofrepetition. For instance, computing device 104 may be configured toperform a single step or sequence repeatedly until a desired orcommanded outcome is achieved; repetition of a step or a sequence ofsteps may be performed iteratively and/or recursively using outputs ofprevious repetitions as inputs to subsequent repetitions, aggregatinginputs and/or outputs of repetitions to produce an aggregate result,reduction or decrement of one or more variables such as globalvariables, and/or division of a larger processing task into a set ofiteratively addressed smaller processing tasks. Computing device 104 mayperform any step or sequence of steps as described in this disclosure inparallel, such as simultaneously and/or substantially simultaneouslyperforming a step two or more times using two or more parallel threads,processor cores, or the like; division of tasks between parallel threadsand/or processes may be performed according to any protocol suitable fordivision of tasks between iterations. Persons skilled in the art, uponreviewing the entirety of this disclosure, will be aware of various waysin which steps, sequences of steps, processing tasks, and/or data may besubdivided, shared, or otherwise dealt with using iteration, recursion,and/or parallel processing.

With continued reference to FIG. 1 , computing device 104 is designedand configured to receive a biological extraction pertaining to a user.A “biological extraction” as used in this disclosure includes at leastan element of user physiological data. As used in this disclosure,“physiological data” is any data indicative of a person's physiologicalstate; physiological state may be evaluated with regard to one or moremeasures of health of a person's body, one or more systems within aperson's body such as a circulatory system, a digestive system, anervous system, or the like, one or more organs within a person's body,and/or any other subdivision of a person's body useful for diagnostic orprognostic purposes. For instance, and without limitation, a particularset of biomarkers, test results, and/or biochemical information may berecognized in a given medical field as useful for identifying variousdisease conditions or prognoses within a relevant field. As anon-limiting example, and without limitation, physiological datadescribing red blood cells, such as red blood cell count, hemoglobinlevels, hematocrit, mean corpuscular volume, mean corpuscularhemoglobin, and/or mean corpuscular hemoglobin concentration may berecognized as useful for identifying various conditions such asdehydration, high testosterone, nutrient deficiencies, kidneydysfunction, chronic inflammation, anemia, and/or blood loss.

With continued reference to FIG. 1 , physiological state data mayinclude, without limitation, hematological data, such as red blood cellcount, which may include a total number of red blood cells in a person'sblood and/or in a blood sample, hemoglobin levels, hematocritrepresenting a percentage of blood in a person and/or sample that iscomposed of red blood cells, mean corpuscular volume, which may be anestimate of the average red blood cell size, mean corpuscularhemoglobin, which may measure average weight of hemoglobin per red bloodcell, mean corpuscular hemoglobin concentration, which may measure anaverage concentration of hemoglobin in red blood cells, platelet count,mean platelet volume which may measure the average size of platelets,red blood cell distribution width, which measures variation in red bloodcell size, absolute neutrophils, which measures the number of neutrophilwhite blood cells, absolute quantities of lymphocytes such as B-cells,T-cells, Natural Killer Cells, and the like, absolute numbers ofmonocytes including macrophage precursors, absolute numbers ofeosinophils, and/or absolute counts of basophils. Physiological statedata may include, without limitation, immune function data such asInterleukine-6 (IL-6), TNF-alpha, systemic inflammatory cytokines, andthe like.

Continuing to refer to FIG. 1 , physiological state data may include,without limitation, data describing blood-born lipids, including totalcholesterol levels, high-density lipoprotein (HDL) cholesterol levels,low-density lipoprotein (LDL) cholesterol levels, very low-densitylipoprotein (VLDL) cholesterol levels, levels of triglycerides, and/orany other quantity of any blood-born lipid or lipid-containingsubstance. Physiological state data may include measures of glucosemetabolism such as fasting glucose levels and/or hemoglobin A1-C (HbA1c)levels. Physiological state data may include, without limitation, one ormore measures associated with endocrine function, such as withoutlimitation, quantities of dehydroepiandrosterone (DHEAS), DHEA-Sulfate,quantities of cortisol, ratio of DHEAS to cortisol, quantities oftestosterone quantities of estrogen, quantities of growth hormone (GH),insulin-like growth factor 1 (IGF-1), quantities of adipokines such asadiponectin, leptin, and/or ghrelin, quantities of somatostatin,progesterone, or the like. Physiological state data may include measuresof estimated glomerular filtration rate (eGFR). Physiological state datamay include quantities of C-reactive protein, estradiol, ferritin,folate, homocysteine, prostate-specific Ag, thyroid-stimulating hormone,vitamin D, 25 hydroxy, blood urea nitrogen, creatinine, sodium,potassium, chloride, carbon dioxide, uric acid, albumin, globulin,calcium, phosphorus, alkaline phosphatase, alanine amino transferase,aspartate amino transferase, lactate dehydrogenase (LDH), bilirubin,gamma-glutamyl transferase (GGT), iron, and/or total iron bindingcapacity (TIBC), or the like. Physiological state data may includeantinuclear antibody levels. Physiological state data may includealuminum levels. Physiological state data may include arsenic levels.Physiological state data may include levels of fibrinogen, plasmacystatin C, and/or brain natriuretic peptide.

Continuing to refer to FIG. 1 , physiological state data may includemeasures of lung function such as forced expiratory volume, one second(FEV-1) which measures how much air can be exhaled in one secondfollowing a deep inhalation, forced vital capacity (FVC), which measuresthe volume of air that may be contained in the lungs. Physiologicalstate data may include a measurement blood pressure, including withoutlimitation systolic and diastolic blood pressure. Physiological statedata may include a measure of waist circumference. Physiological statedata may include body mass index (BMI). Physiological state data mayinclude one or more measures of bone mass and/or density such asdual-energy x-ray absorptiometry. Physiological state data may includeone or more measures of muscle mass. Physiological state data mayinclude one or more measures of physical capability such as withoutlimitation measures of grip strength, evaluations of standing balance,evaluations of gait speed, pegboard tests, timed up and go tests, and/orchair rising tests.

Still viewing FIG. 1 , physiological state data may include one or moremeasures of cognitive function, including without limitation Reyauditory verbal learning test results, California verbal learning testresults, NIH toolbox picture sequence memory test, Digital symbol codingevaluations, and/or Verbal fluency evaluations. Physiological state datamay include one or more evaluations of sensory ability, includingmeasures of audition, vision, olfaction, gustation, vestibular functionand pain.

Continuing to refer to FIG. 1 , physiological state data may includepsychological data. Psychological data may include any data generatedusing psychological, neuro-psychological, and/or cognitive evaluations,as well as diagnostic screening tests, personality tests, personalcompatibility tests, or the like; such data may include, withoutlimitation, numerical score data entered by an evaluating professionaland/or by a subject performing a self-test such as a computerizedquestionnaire. Psychological data may include textual, video, or imagedata describing testing, analysis, and/or conclusions entered by amedical professional such as without limitation a psychologist,psychiatrist, psychotherapist, social worker, a medical doctor, or thelike. Psychological data may include data gathered from userinteractions with persons, documents, and/or computing devices 104; forinstance, user patterns of purchases, including electronic purchases,communication such as via chat-rooms or the like, any textual, image,video, and/or data produced by the subject, any textual image, videoand/or other data depicting and/or describing the subject, or the like.Any psychological data and/or data used to generate psychological datamay be analyzed using machine-learning and/or language processing moduleas described in this disclosure.

Still referring to FIG. 1 , physiological state data may include genomicdata, including deoxyribonucleic acid (DNA) samples and/or sequences,such as without limitation DNA sequences contained in one or morechromosomes in human cells. Genomic data may include, withoutlimitation, ribonucleic acid (RNA) samples and/or sequences, such assamples and/or sequences of messenger RNA (mRNA) or the like taken fromhuman cells. Genetic data may include telomere lengths. Genomic data mayinclude epigenetic data including data describing one or more states ofmethylation of genetic material. Physiological state data may includeproteomic data, which as used herein is data describing all proteinsproduced and/or modified by an organism, colony of organisms, or systemof organisms, and/or a subset thereof. Physiological state data mayinclude data concerning a microbiome of a person, which as used hereinincludes any data describing any microorganism and/or combination ofmicroorganisms living on or within a person, including withoutlimitation biomarkers, genomic data, proteomic data, and/or any othermetabolic or biochemical data useful for analysis of the effect of suchmicroorganisms on other physiological state data of a person, asdescribed in further detail below.

With continuing reference to FIG. 1 , physiological state data mayinclude one or more user-entered descriptions of a person'sphysiological state. One or more user-entered descriptions may include,without limitation, user descriptions of symptoms, which may includewithout limitation current or past physical, psychological, perceptual,and/or neurological symptoms, user descriptions of current or pastphysical, emotional, and/or psychological problems and/or concerns, userdescriptions of past or current treatments, including therapies,nutritional regimens, exercise regimens, pharmaceuticals or the like, orany other user-entered data that a user may provide to a medicalprofessional when seeking treatment and/or evaluation, and/or inresponse to medical intake papers, questionnaires, questions frommedical professionals, or the like. Physiological state data may includeany physiological state data, as described above, describing anymulticellular organism living in or on a person including any parasiticand/or symbiotic organisms living in or on the persons; non-limitingexamples may include mites, nematodes, flatworms, or the like. Examplesof physiological state data described in this disclosure are presentedfor illustrative purposes only and are not meant to be exhaustive.

With continued reference to FIG. 1 , physiological data may include,without limitation any result of any medical test, physiologicalassessment, cognitive assessment, psychological assessment, or the like.System 100 may receive at least a physiological data from one or moreother devices after performance; system 100 may alternatively oradditionally perform one or more assessments and/or tests to obtain atleast a physiological data, and/or one or more portions thereof, onsystem 100. For instance, at least physiological data may include ormore entries by a user in a form or similar graphical user interfaceobject; one or more entries may include, without limitation, userresponses to questions on a psychological, behavioral, personality, orcognitive test. For instance, at least a server may present to user aset of assessment questions designed or intended to evaluate a currentstate of mind of the user, a current psychological state of the user, apersonality trait of the user, or the like; at least a server mayprovide user-entered responses to such questions directly as at least aphysiological data and/or may perform one or more calculations or otheralgorithms to derive a score or other result of an assessment asspecified by one or more testing protocols, such as automatedcalculation of a Stanford-Binet and/or Wechsler scale for IQ testing, apersonality test scoring such as a Myers-Briggs test protocol, or otherassessments that may occur to persons skilled in the art upon reviewingthe entirety of this disclosure.

With continued reference to FIG. 1 , assessment and/or self-assessmentdata, and/or automated or other assessment results, obtained from athird-party device; third-party device may include, without limitation,a server or other device (not shown) that performs automated cognitive,psychological, behavioral, personality, or other assessments.Third-party device may include a device operated by an informed advisor.An informed advisor may include any medical professional who may assistand/or participate in the medical treatment of a user. An informedadvisor may include a medical doctor, nurse, physician assistant,pharmacist, yoga instructor, nutritionist, spiritual healer, meditationteacher, fitness coach, health coach, life coach, and the like.

With continued reference to FIG. 1 , physiological data may include datadescribing one or more test results, including results of mobilitytests, stress tests, dexterity tests, endocrinal tests, genetic tests,and/or electromyographic tests, biopsies, radiological tests, genetictests, and/or sensory tests. Persons skilled in the art, upon reviewingthe entirety of this disclosure, will be aware of various additionalexamples of at least a physiological sample consistent with thisdisclosure.

With continued reference to FIG. 1 , physiological data may include oneor more user body measurements. A “user body measurement” as used inthis disclosure, includes a measurable indicator of the severity,absence, and/or presence of a disease state. A “disease state” as usedin this disclosure, includes any harmful deviation from the normalstructural and/or function state of a human being. A disease state mayinclude any medical condition and may be associated with specificsymptoms and signs. A disease state may be classified into differenttypes including infectious diseases, deficiency diseases, hereditarydiseases, and/or physiological diseases. For instance and withoutlimitation, internal dysfunction of the immune system may produce avariety of different diseases including immunodeficiency,hypersensitivity, allergies, and/or autoimmune disorders.

With continued reference to FIG. 1 , user body measurements may berelated to particular dimensions of the human body. A “dimension of thehuman body” as used in this disclosure, includes one or more functionalbody systems that are impaired by disease in a human body and/or animalbody. Functional body systems may include one or more body systemsrecognized as attributing to root causes of disease by functionalmedicine practitioners and experts. A “root cause” as used in thisdisclosure, includes any chain of causation describing underlyingreasons for a particular disease state and/or medical condition insteadof focusing solely on symptomatology reversal. Root cause may includechains of causation developed by functional medicine practices that mayfocus on disease causation and reversal. For instance and withoutlimitation, a medical condition such as diabetes may include a chain ofcausation that does not include solely impaired sugar metabolism butthat also includes impaired hormone systems including insulinresistance, high cortisol, less than optimal thyroid production, and lowsex hormones. Diabetes may include further chains of causation thatinclude inflammation, poor diet, delayed food allergies, leaky gut,oxidative stress, damage to cell membranes, and dysbiosis. Dimensions ofthe human body may include but are not limited to epigenetics, gut-wall,microbiome, nutrients, genetics, and/or metabolism.

With continued reference to FIG. 1 , epigenetic, as used herein,includes any user body measurements describing changes to a genome thatdo not involve corresponding changes in nucleotide sequence. Epigeneticbody measurement may include data describing any heritable phenotypic.Phenotype, as used herein, include any observable trait of a userincluding morphology, physical form, and structure. Phenotype mayinclude a user's biochemical and physiological properties, behavior, andproducts of behavior. Behavioral phenotypes may include cognitive,personality, and behavior patterns. This may include effects on cellularand physiological phenotypic traits that may occur due to external orenvironmental factors. For example, DNA methylation and histonemodification may alter phenotypic expression of genes without alteringunderlying DNA sequence. Epigenetic body measurements may include datadescribing one or more states of methylation of genetic material.

With continued reference to FIG. 1 , gut-wall, as used herein, includesthe space surrounding the lumen of the gastrointestinal tract that iscomposed of four layers including the mucosa, submucosa, muscular layer,and serosa. The mucosa contains the gut epithelium that is composed ofgoblet cells that function to secrete mucus, which aids in lubricatingthe passage of food throughout the digestive tract. The goblet cellsalso aid in protecting the intestinal wall from destruction by digestiveenzymes. The mucosa includes villi or folds of the mucosa located in thesmall intestine that increase the surface area of the intestine. Thevilli contain a lacteal, that is a vessel connected to the lymph systemthat aids in removal of lipids and tissue fluids. Villi may containmicrovilli that increase the surface area over which absorption can takeplace. The large intestine lack villi and instead a flat surfacecontaining goblet cells are present.

With continued reference to FIG. 1 , gut-wall includes the submucosa,which contains nerves, blood vessels, and elastic fibers containingcollagen. Elastic fibers contained within the submucosa aid instretching the gastrointestinal tract with increased capacity while alsomaintaining the shape of the intestine. Gut-wall includes muscular layerwhich contains smooth muscle that aids in peristalsis and the movementof digested material out of and along the gut. Gut-wall includes theserosa which is composed of connective tissue and coated in mucus toprevent friction damage from the intestine rubbing against other tissue.Mesenteries are also found in the serosa and suspend the intestine inthe abdominal cavity to stop it from being disturbed when a person isphysically active.

With continued reference to FIG. 1 , gut-wall body measurement mayinclude data describing one or more test results including results ofgut-wall function, gut-wall integrity, gut-wall strength, gut-wallabsorption, gut-wall permeability, intestinal absorption, gut-wallbarrier function, gut-wall absorption of bacteria, gut-wallmalabsorption, gut-wall gastrointestinal imbalances and the like.

With continued reference to FIG. 1 , gut-wall body measurement mayinclude any data describing blood test results of creatinine levels,lactulose levels, zonulin levels, and mannitol levels. Gut-wall bodymeasurement may include blood test results of specific gut-wall bodymeasurements including d-lactate, endotoxin lipopolysaccharide (LPS)Gut-wall body measurement may include data breath tests measuringlactulose, hydrogen, methane, lactose, and the like. Gut-wall bodymeasurement may include blood test results describing blood chemistrylevels of albumin, bilirubin, complete blood count, electrolytes,minerals, sodium, potassium, calcium, glucose, blood clotting factors,

With continued reference to FIG. 1 , gut-wall body measurement mayinclude one or more stool test results describing presence or absence ofparasites, firmicutes, Bacteroidetes, absorption, inflammation, foodsensitivities. Stool test results may describe presence, absence, and/ormeasurement of acetate, aerobic bacterial cultures, anerobic bacterialcultures, fecal short chain fatty acids, beta-glucuronidase,cholesterol, chymotrypsin, fecal color, cryptosporidium EIA, Entamoebahistolytica, fecal lactoferrin, Giardia lamblia EIA, long chain fattyacids, meat fibers and vegetable fibers, mucus, occult blood, parasiteidentification, phospholipids, propionate, putrefactive short chainfatty acids, total fecal fat, triglycerides, yeast culture, n-butyrate,pH and the like.

With continued reference to FIG. 1 , gut-wall body measurement mayinclude one or more stool test results describing presence, absence,and/or measurement of microorganisms including bacteria, archaea, fungi,protozoa, algae, viruses, parasites, worms, and the like. Stool testresults may contain species such as Bifidobacterium species,campylobacter species, Clostridium difficile, cryptosporidium species,Cyclospora cayetanensis, Cryptosporidium EIA, Dientamoeba fragilis,Entamoeba histolytica, Escherichia coli, Entamoeba histolytica, Giardia,H. pylori, Candida albicans, Lactobacillus species, worms, macroscopicworms, mycology, protozoa, Shiga toxin E. coli, and the like.

With continued reference to FIG. 1 , gut-wall body measurement mayinclude one or more microscopic ova exam results, microscopic parasiteexam results, protozoan polymerase chain reaction test results and thelike. Gut-wall body measurement may include enzyme-linked immunosorbentassay (ELISA) test results describing immunoglobulin G (Ig G) foodantibody results, immunoglobulin E (Ig E) food antibody results, Ig Emold results, IgG spice and herb results. Gut-wall body measurement mayinclude measurements of calprotectin, eosinophil protein x (EPX), stoolweight, pancreatic elastase, total urine volume, blood creatininelevels, blood lactulose levels, blood mannitol levels.

With continued reference to FIG. 1 , gut-wall body measurement mayinclude one or more elements of data describing one or more proceduresexamining gut including for example colonoscopy, endoscopy, large andsmall molecule challenge and subsequent urinary recovery using largemolecules such as lactulose, polyethylene glycol-3350, and smallmolecules such as mannitol, L-rhamnose, polyethyleneglycol-300. Gut-wallbody measurement may include data describing one or more images such asx-ray, MRI, CT scan, ultrasound, standard barium follow-throughexamination, barium enema, barium with contract, MRI fluoroscopy,positron emission tomography 9PET), diffusion-weighted MRI imaging, andthe like.

With continued reference to FIG. 1 , microbiome, as used herein,includes ecological community of commensal, symbiotic, and pathogenicmicroorganisms that reside on or within any of a number of human tissuesand biofluids. For example, human tissues and biofluids may include theskin, mammary glands, placenta, seminal fluid, uterus, vagina, ovarianfollicles, lung, saliva, oral mucosa, conjunctiva, biliary, andgastrointestinal tracts. Microbiome may include for example, bacteria,archaea, protists, fungi, and viruses. Microbiome may include commensalorganisms that exist within a human being without causing harm ordisease. Microbiome may include organisms that are not harmful butrather harm the human when they produce toxic metabolites such astrimethylamine. Microbiome may include pathogenic organisms that causehost damage through virulence factors such as producing toxicby-products. Microbiome may include populations of microbes such asbacteria and yeasts that may inhabit the skin and mucosal surfaces invarious parts of the body. Bacteria may include for example Firmicutesspecies, Bacteroidetes species, Proteobacteria species, Verrumicrobiaspecies, Actinobacteria species, Fusobacteria species, Cyanobacteriaspecies and the like. Archaea may include methanogens such asMethanobrevibacter smithies' and Methanosphaera stadtmanae. Fungi mayinclude Candida species and Malassezia species. Viruses may includebacteriophages. Microbiome species may vary in different locationsthroughout the body. For example, the genitourinary system may contain ahigh prevalence of Lactobacillus species while the gastrointestinaltract may contain a high prevalence of Bifidobacterium species while thelung may contain a high prevalence of Streptococcus and Staphylococcusspecies.

With continued reference to FIG. 1 , microbiome body measurement mayinclude one or more stool test results describing presence, absence,and/or measurement of microorganisms including bacteria, archaea, fungi,protozoa, algae, viruses, parasites, worms, and the like. Stool testresults may contain species such as Ackerman's muciniphila,Anaerotruncus colihominis, bacteriology, Bacteroides vulgates',Bacteroides-Prevotella, Barnesiella species, Bifidobacterium longarm,Bifidobacterium species, Butyrivbrio crossotus, Clostridium species,Collinsella aerofaciens, fecal color, fecal consistency, Coprococcuseutactus, Desulfovibrio piger, Escherichia coli, Faecalibacteriumprausnitzii, Fecal occult blood, Firmicutes to Bacteroidetes ratio,Fusobacterium species, Lactobacillus species, Methanobrevibactersmithii, yeast minimum inhibitory concentration, bacteria minimuminhibitory concentration, yeast mycology, fungi mycology, Odoribacterspecies, Oxalobacter formigenes, parasitology, Prevotella species,Pseudoflavonifractor species, Roseburia species, Ruminococcus species,Veillonella species and the like.

With continued reference to FIG. 1 , microbiome body measurement mayinclude one or more stool tests results that identify all microorganismsliving a user's gut including bacteria, viruses, archaea, yeast, fungi,parasites, and bacteriophages. Microbiome body measurement may includeDNA and RNA sequences from live microorganisms that may impact a user'shealth. Microbiome body measurement may include high resolution of bothspecies and strains of all microorganisms. Microbiome body measurementmay include data describing current microbe activity. Microbiome bodymeasurement may include expression of levels of active microbial genefunctions. Microbiome body measurement may include descriptions ofsources of disease-causing microorganisms, such as viruses found in thegastrointestinal tract such as raspberry bushy swarf virus fromconsuming contaminated raspberries or Pepino mosaic virus from consumingcontaminated tomatoes.

With continued reference to FIG. 1 , microbiome body measurement mayinclude one or more blood test results that identify metabolitesproduced by microorganisms. Metabolites may include for example,indole-3-propionic acid, indole-3-lactic acid, indole-3-acetic acid,tryptophan, serotonin, kynurenine, total indoxyl sulfate, tyrosine,xanthine, 3-methylxanthine, uric acid, and the like.

With continued reference to FIG. 1 , microbiome body measurement mayinclude one or more breath test results that identify certain strains ofmicroorganisms that may be present in certain areas of a user's body.This may include for example, lactose intolerance breath tests,methane-based breath tests, hydrogen-based breath tests, fructose-basedbreath tests, Helicobacter pylori breath test, fructose intolerancebreath test, bacterial overgrowth syndrome breath tests and the like.

With continued reference to FIG. 1 , microbiome body measurement mayinclude one or more urinary analysis results for certain microbialstrains present in urine. This may include for example, urinalysis thatexamines urine specific gravity, urine cytology, urine sodium, urineculture, urinary calcium, urinary hematuria, urinary glucose levels,urinary acidity, urinary protein, urinary nitrites, bilirubin, red bloodcell urinalysis, and the like.

With continued reference to FIG. 1 , nutrient as used herein, includesany substance required by the human body to function. Nutrients mayinclude carbohydrates, protein, lipids, vitamins, minerals,antioxidants, fatty acids, amino acids, and the like. Nutrients mayinclude for example vitamins such as thiamine, riboflavin, niacin,pantothenic acid, pyridoxine, biotin, folate, cobalamin, Vitamin C,Vitamin A, Vitamin D, Vitamin E, and Vitamin K. Nutrients may includefor example minerals such as sodium, chloride, potassium, calcium,phosphorous, magnesium, sulfur, iron, zinc, iodine, selenium, copper,manganese, fluoride, chromium, molybdenum, nickel, aluminum, silicon,vanadium, arsenic, and boron.

With continued reference to FIG. 1 , nutrients may include extracellularnutrients that are free floating in blood and exist outside of cells.Extracellular nutrients may be located in serum. Nutrients may includeintracellular nutrients which may be absorbed by cells including whiteblood cells and red blood cells.

With continued reference to FIG. 1 , nutrient body measurement mayinclude one or more blood test results that identify extracellular andintracellular levels of nutrients. Nutrient body measurement may includeblood test results that identify serum, white blood cell, and red bloodcell levels of nutrients. For example, nutrient body measurement mayinclude serum, white blood cell, and red blood cell levels ofmicronutrients such as Vitamin A, Vitamin B1, Vitamin B2, Vitamin B3,Vitamin B6, Vitamin B12, Vitamin B5, Vitamin C, Vitamin D, Vitamin E,Vitamin K1, Vitamin K2, and folate.

With continued reference to FIG. 1 , nutrient body measurement mayinclude one or more blood test results that identify serum, white bloodcell and red blood cell levels of nutrients such as calcium, manganese,zinc, copper, chromium, iron, magnesium, copper to zinc ratio, choline,inositol, carnitine, methylmalonic acid (MMA), sodium, potassium,asparagine, glutamine, serine, coenzyme q10, cysteine, alpha lipoicacid, glutathione, selenium, eicosapentaenoic acid (EPA),docosahexaenoic acid (DHA), docosapentaenoic acid (DPA), total omega-3,lauric acid, arachidonic acid, oleic acid, total omega 6, and omega 3index.

With continued reference to FIG. 1 , nutrient body measurement mayinclude one or more salivary test results that identify levels ofnutrients including any of the nutrients as described herein. Nutrientbody measurement may include hair analysis of levels of nutrientsincluding any of the nutrients as described herein.

With continued reference to FIG. 1 , genetic as used herein, includesany inherited trait. Inherited traits may include genetic materialcontained with DNA including for example, nucleotides. Nucleotidesinclude adenine (A), cytosine (C), guanine (G), and thymine (T). Geneticinformation may be contained within the specific sequence of anindividual's nucleotides and sequence throughout a gene or DNA chain.Genetics may include how a particular genetic sequence may contribute toa tendency to develop a certain disease such as cancer or Alzheimer'sdisease.

With continued reference to FIG. 1 , genetic body measurement mayinclude one or more results from one or more blood tests, hair tests,skin tests, urine, amniotic fluid, buccal swabs and/or tissue test toidentify a user's particular sequence of nucleotides, genes,chromosomes, and/or proteins. Genetic body measurement may include teststhat example genetic changes that may lead to genetic disorders. Geneticbody measurement may detect genetic changes such as deletion of geneticmaterial or pieces of chromosomes that may cause Duchenne MuscularDystrophy. Genetic body measurement may detect genetic changes such asinsertion of genetic material into DNA or a gene such as the BRCA1 genethat is associated with an increased risk of breast and ovarian cancerdue to insertion of 2 extra nucleotides. Genetic body measurement mayinclude a genetic change such as a genetic substitution from a piece ofgenetic material that replaces another as seen with sickle cell anemiawhere one nucleotide is substituted for another. Genetic bodymeasurement may detect a genetic change such as a duplication when extragenetic material is duplicated one or more times within a person'sgenome such as with Charcot-Marie Tooth disease type 1. Genetic bodymeasurement may include a genetic change such as an amplification whenthere is more than a normal number of copies of a gene in a cell such asHER2 amplification in cancer cells. Genetic body measurement may includea genetic change such as a chromosomal translocation when pieces ofchromosomes break off and reattach to another chromosome such as withthe BCR-ABL1 gene sequence that is formed when pieces of chromosome 9and chromosome 22 break off and switch places. Genetic body measurementmay include a genetic change such as an inversion when one chromosomeexperiences two breaks and the middle piece is flipped or invertedbefore reattaching. Genetic body measurement may include a repeat suchas when regions of DNA contain a sequence of nucleotides that repeat anumber of times such as for example in Huntington's disease or Fragile Xsyndrome. Genetic body measurement may include a genetic change such asa trisomy when there are three chromosomes instead of the usual pair asseen with Down syndrome with a trisomy of chromosome 21, Edwardssyndrome with a trisomy at chromosome 18 or Patau syndrome with atrisomy at chromosome 13. Genetic body measurement may include a geneticchange such as monosomy such as when there is an absence of a chromosomeinstead of a pair, such as in Turner syndrome.

With continued reference to FIG. 1 , genetic body measurement mayinclude an analysis of COMT gene that is responsible for producingenzymes that metabolize neurotransmitters. Genetic body measurement mayinclude an analysis of DRD2 gene that produces dopamine receptors in thebrain. Genetic body measurement may include an analysis of ADRA2B genethat produces receptors for noradrenaline. Genetic body measurement mayinclude an analysis of 5-HTTLPR gene that produces receptors forserotonin. Genetic body measurement may include an analysis of BDNF genethat produces brain derived neurotrophic factor. Genetic bodymeasurement may include an analysis of 9p21 gene that is associated withcardiovascular disease risk. Genetic body measurement may include ananalysis of APOE gene that is involved in the transportation of bloodlipids such as cholesterol. Genetic body measurement may include ananalysis of NOS3 gene that is involved in producing enzymes involved inregulating vasodilation and vasoconstriction of blood vessels.

With continued reference to FIG. 1 , genetic body measurement mayinclude ACE gene that is involved in producing enzymes that regulateblood pressure. Genetic body measurement may include SLCO1B1 gene thatdirects pharmaceutical compounds such as statins into cells. Geneticbody measurement may include FUT2 gene that produces enzymes that aid inabsorption of Vitamin B12 from digestive tract. Genetic body measurementmay include MTHFR gene that is responsible for producing enzymes thataid in metabolism and utilization of Vitamin B9 or folate. Genetic bodymeasurement may include SHMT1 gene that aids in production andutilization of Vitamin B9 or folate. Genetic body measurement mayinclude MTRR gene that produces enzymes that aid in metabolism andutilization of Vitamin B12. Genetic body measurement may include MTRgene that produces enzymes that aid in metabolism and utilization ofVitamin B12. Genetic body measurement may include FTO gene that aids infeelings of satiety or fullness after eating. Genetic body measurementmay include MC4R gene that aids in producing hunger cues and hungertriggers. Genetic body measurement may include APOA2 gene that directsbody to produce ApoA2 thereby affecting absorption of saturated fats.Genetic body measurement may include UCP1 gene that aids in controllingmetabolic rate and thermoregulation of body. Genetic body measurementmay include TCF7L2 gene that regulates insulin secretion. Genetic bodymeasurement may include AMY1 gene that aids in digestion of starchyfoods. Genetic body measurement may include MCM6 gene that controlsproduction of lactase enzyme that aids in digesting lactose found indairy products. Genetic body measurement may include BCMO1 gene thataids in producing enzymes that aid in metabolism and activation ofVitamin A. Genetic body measurement may include SLC23A1 gene thatproduce and transport Vitamin C. Genetic body measurement may includeCYP2R1 gene that produce enzymes involved in production and activationof Vitamin D. Genetic body measurement may include GC gene that produceand transport Vitamin D. Genetic body measurement may include CYP1A2gene that aid in metabolism and elimination of caffeine. Genetic bodymeasurement may include CYP17A1 gene that produce enzymes that convertprogesterone into androgens such as androstenedione, androstendiol,dehydroepiandrosterone, and testosterone.

With continued reference to FIG. 1 , genetic body measurement mayinclude CYP19A1 gene that produce enzymes that convert androgens such asandrostenedione and testosterone into estrogens including estradiol andestrone. Genetic body measurement may include SRD5A2 gene that aids inproduction of enzymes that convert testosterone intodihydrotestosterone. Genetic body measurement may include UFT2B17 genethat produces enzymes that metabolize testosterone anddihydrotestosterone. Genetic body measurement may include CYP1A1 genethat produces enzymes that metabolize estrogens into 2 hydroxy-estrogen.Genetic body measurement may include CYP1B1 gene that produces enzymesthat metabolize estrogens into 4 hydroxy-estrogen. Genetic bodymeasurement may include CYP3A4 gene that produces enzymes thatmetabolize estrogen into 16 hydroxy-estrogen. Genetic body measurementmay include COMT gene that produces enzymes that metabolize 2hydroxy-estrogen and 4 hydroxy-estrogen into methoxy estrogen. Geneticbody measurement may include GSTT1 gene that produces enzymes thateliminate toxic by-products generated from metabolism of estrogens.Genetic body measurement may include GSTM1 gene that produces enzymesresponsible for eliminating harmful by-products generated frommetabolism of estrogens. Genetic body measurement may include GSTP1 genethat produces enzymes that eliminate harmful by-products generated frommetabolism of estrogens. Genetic body measurement may include SOD2 genethat produces enzymes that eliminate oxidant by-products generated frommetabolism of estrogens.

With continued reference to FIG. 1 , metabolic, as used herein, includesany process that converts food and nutrition into energy. Metabolic mayinclude biochemical processes that occur within the body. Metabolic bodymeasurement may include blood tests, hair tests, skin tests, amnioticfluid, buccal swabs and/or tissue test to identify a user's metabolism.Metabolic body measurement may include blood tests that examine glucoselevels, electrolytes, fluid balance, kidney function, and liverfunction. Metabolic body measurement may include blood tests thatexamine calcium levels, albumin, total protein, chloride levels, sodiumlevels, potassium levels, carbon dioxide levels, bicarbonate levels,blood urea nitrogen, creatinine, alkaline phosphatase, alanine aminotransferase, aspartate amino transferase, bilirubin, and the like.

With continued reference to FIG. 1 , metabolic body measurement mayinclude one or more blood, saliva, hair, urine, skin, and/or buccalswabs that examine levels of hormones within the body such as11-hydroxy-androstereone, 11-hydroxy-etiocholanolone,11-keto-androsterone, 11-keto-etiocholanolone, 16 alpha-hydroxyestrone,2-hydroxyestrone, 4-hydroxyestrone, 4-methoxyestrone, androstanediol,androsterone, creatinine, DHEA, estradiol, estriol, estrone,etiocholanolone, pregnanediol, pregnanestriol, specific gravity,testosterone, tetrahydrocortisol, tetrahydrocrotisone,tetrahydrodeoxycortisol, allo-tetrahydrocortisol.

With continued reference to FIG. 1 , metabolic body measurement mayinclude one or more metabolic rate test results such as breath teststhat may analyze a user's resting metabolic rate or number of caloriesthat a user's body burns each day rest. Metabolic body measurement mayinclude one or more vital signs including blood pressure, breathingrate, pulse rate, temperature, and the like. Metabolic body measurementmay include blood tests such as a lipid panel such as low densitylipoprotein (LDL), high density lipoprotein (HDL), triglycerides, totalcholesterol, ratios of lipid levels such as total cholesterol to HDLratio, insulin sensitivity test, fasting glucose test, Hemoglobin A1Ctest, adipokines such as leptin and adiponectin, neuropeptides such asghrelin, pro-inflammatory cytokines such as interleukin 6 or tumornecrosis factor alpha, anti-inflammatory cytokines such as interleukin10, markers of antioxidant status such as oxidized low-densitylipoprotein, uric acid, paraoxonase 1. Persons skilled in the art, uponreviewing the entirety of this disclosure, will be aware of variousadditional examples of physiological state data that may be usedconsistently with descriptions of systems and methods as provided inthis disclosure.

With continued reference to FIG. 1 , physiological data may be obtainedfrom a physically extracted sample. A “physical sample” as used in thisexample, may include any sample obtained from a human body of a user. Aphysical sample may be obtained from a bodily fluid and/or tissueanalysis such as a blood sample, tissue, sample, buccal swab, mucoussample, stool sample, hair sample, fingernail sample and the like. Aphysical sample may be obtained from a device in contact with a humanbody of a user such as a microchip embedded in a user's skin, a sensorin contact with a user's skin, a sensor located on a user's tooth, andthe like. Physiological data may be obtained from a physically extractedsample. A physical sample may include a signal from a sensor configuredto detect physiological data of a user and record physiological data asa function of the signal. A sensor may include any medical sensor and/ormedical device configured to capture sensor data concerning a patient,including any scanning, radiological and/or imaging device such aswithout limitation x-ray equipment, computer assisted tomography (CAT)scan equipment, positron emission tomography (PET) scan equipment, anyform of magnetic resonance imagery (MM) equipment, ultrasound equipment,optical scanning equipment such as photo-plethysmographic equipment, orthe like. A sensor may include any electromagnetic sensor, includingwithout limitation electroencephalographic sensors,magnetoencephalographic sensors, electrocardiographic sensors,electromyographic sensors, or the like. A sensor may include atemperature sensor. A sensor may include any sensor that may be includedin a mobile device and/or wearable device, including without limitationa motion sensor such as an inertial measurement unit (IMU), one or moreaccelerometers, one or more gyroscopes, one or more magnetometers, orthe like. At least a wearable and/or mobile device sensor may capturestep, gait, and/or other mobility data, as well as data describingactivity levels and/or physical fitness. At least a wearable and/ormobile device sensor may detect heart rate or the like. A sensor maydetect any hematological parameter including blood oxygen level, pulserate, heart rate, pulse rhythm, blood sugar, and/or blood pressure. Asensor may be configured to detect internal and/or external biomarkersand/or readings. A sensor may be a part of system 100 or may be aseparate device in communication with system 100. User data may includea profile, such as a psychological profile, generated using previousitem selections by the user; profile may include, without limitation, aset of actions and/or navigational actions performed as described infurther detail below, which may be combined with biological extractiondata and/or other user data for processes as described in further detailbelow.

Physiological data and/or other data of each user may be stored, withoutlimitation, in a user database 108. User database 108 may include anydata structure for ordered storage and retrieval of data, which may beimplemented as a hardware or software module. A user database 108 may beimplemented, without limitation, as a relational database, a key-valueretrieval datastore such as a NOSQL database, or any other format orstructure for use as a datastore that a person skilled in the art wouldrecognize as suitable upon review of the entirety of this disclosure. Auser database 108 may include a plurality of data entries and/or recordscorresponding to user tests as described above. Data entries in a userdatabase 108 may be flagged with or linked to one or more additionalelements of information, which may be reflected in data entry cellsand/or in linked tables such as tables related by one or more indices ina relational database. Persons skilled in the art, upon reviewing theentirety of this disclosure, will be aware of various ways in which dataentries in a user database 108 may reflect categories, cohorts, and/orpopulations of data consistently with this disclosure. User database 108may be located in memory of computing device 104 and/or on anotherdevice in and/or in communication with system 100.

Referring now to FIG. 2 , an exemplary embodiment of a user database 108is illustrated. One or more tables in user database 108 may include,without limitation, a biological extraction table 200, which may be usedto store biological extraction data. User database 108 may include abehavioral history table 204, where current or past reports orinformation indicative of user behavior, including without limitationnegative lifestyle behaviors, may be stored; behavioral history table204 may store, as a non-limiting example, records of reports receivedfrom user and/or other persons and/or devices indicating engagement inone or more negative lifestyle behaviors as described in thisdisclosure. User database 108 may include a demographic table 112, whichmay include a demographic table 208; demographic table may include anydemographic information concerning a user, including without limitationage, sex, national origin, ethnicity, language, religious affiliation,and the like. User database 108 may include a proscription table 112,which may store one or more user proscriptions and/or user beliefproscriptions as described in further detail below. Persons skilled inthe art, upon reviewing the entirety of this disclosure, will be awareof various alternative or additional data which may be stored in userdatabase 108, including without limitation any data concerning any useractivity, demographics, profile information, viewing and/or mediaconsumption history, or the like.

Still referring to FIG. 1 , computing device 104 is designed andconfigured to generate a plurality of lifestyle interventioncombinations as a function of biological extraction using a firstmachine-learning process 112. A “lifestyle intervention combination” isa set of actions, called “intervention elements,” a user can take that,taken together, act to improve the user's physical condition. Each suchintervention element may be stored in an intervention element database116, which may be implemented in any manner suitable for implementationof user database 108 as described above.

Referring now to FIG. 3 , an exemplary embodiment of interventionelement database 116 is illustrated. Intervention element database 116may include a dietary change table 300, which may contain, withoutlimitation, intervention elements corresponding to dietary changes, suchas without limitation a reduction in the daily consumption of aparticular nutrient, an increase in the daily consumption of aparticular nutrient, a decrease in the daily consumption of a givencategory of food, an increase in the daily consumption of a givencategory of food, or the like. For instance, and without limitation, anintervention element may include cessation of meat consumption, anaddition of one serving of fruit per day, a halving of daily saturatedfat intake as measured in calories and/or grams of saturated fat, or thelike. Intervention elements pertaining to nutritional goals may listparticular meals, meal plans, food elements, or the like, together withcorresponding nutritional goals met by such meals, meal plans, and/orfood elements. Intervention element database 116 may include an exerciseelement table 304, which may contain intervention elements that includeone or more measurable exercise goals, such as a goal to take sometarget number of steps per day, a goal to burn a target number ofcalories per day, a goal to engage in a certain amount of cardiovascularexercise at a given intensity level, as represented for instance by anumber on a discrete scale from 1 to 10, where 1 is a minimal intensityand 10 is a maximal intensity, a goal to engage in a certain amount ofresistance training at a given intensity level, which may be similarlyrepresented, a goal to spend a certain quantity of time per daystretching, or the like. Intervention elements pertaining to exercisegoals may include particular forms of exercise, such as jogging, biking,weightlifting, or the like, which may list corresponding exercise goalsthat match the intervention elements. Intervention element database 116may include, without limitation, a sleep table 308, which may record oneor more intervention elements to sleep goals, such as a goal to sleep acertain number of hours per week or per day, to set a fixed bedtime, orthe like. Intervention element table may include, without limitation, anegative habit table 312, which may record information elements relatingto a cessation or reduction of a negative habit, such as tobaccoconsumption, alcohol consumption, gambling, drug use, or the like; suchintervention elements may alternatively or additionally list particularprograms and/or protocols for reduction in bad habits, such as 12-stepprograms or the like.

Referring again to FIG. 1 , computing device 104 may generate eachlifestyle intervention combination of plurality of lifestyleintervention combinations by combining intervention elements, which maybe retrieved from intervention element database 116. Interventionelements for combinations may be selected according to interventionelements likely to improve a particular user's state of health; suchelements may be identified, without limitation, using expert inputs; forinstance expert inputs may link particular endocrinal levels and/orchange in endocrinal levels to particular nutritional goals, exercisegoals, sleep goals, or cessation of bad habits, which may in turn beused to retrieve particular intervention elements from interventionelement database 116. As another non-limiting example, one or moreexpert inputs may identify reductions in bad habits that may improveuser health, one or more programs that may aid in cessation of one ormore bad habits, or the like. One or more expert inputs may propose oneor more combinations of intervention elements that an expert may opineare especially useful, and/or that an expert may have viewed in the pastas efficacious or convenient. Expert opinions may be stored in and/orretrieved from an expert database 120, which may include any componentand/or module suitable for use as user database 108 as described above.

Referring now to FIG. 4 , an exemplary embodiment of an expert database120 is illustrated. Expert database 120 may, as a non-limiting example,organize data stored in the expert database 120 according to one or moredatabase tables. One or more database tables may be linked to oneanother by, for instance, common column values. For instance, a commoncolumn between two tables of expert database 120 may include anidentifier of an expert submission, such as a form entry, textualsubmission, expert paper, or the like, for instance as defined below; asa result, a query may be able to retrieve all rows from any tablepertaining to a given submission or set thereof. Other columns mayinclude any other category usable for organization or subdivision ofexpert data, including types of expert data, names and/or identifiers ofexperts submitting the data, times of submission, or the like; personsskilled in the art, upon reviewing the entirety of this disclosure, willbe aware of various ways in which expert data from one or more tablesmay be linked and/or related to expert data in one or more other tables.

Still referring to FIG. 4 , one or more database tables in expertdatabase 120 may include, as a non-limiting example, an expertextraction table 400, which may record expert submission datacorresponding to biological extraction data as described above. Tablesmay include an expert habit table 404, which may record expertsubmission data describing one or more negative lifestyle habits asdescribed in further detail below, as well as relationships thereof withbiological extractions. Tables may include an expert intervention table408, which may record expert submission data describing one or moreintervention elements and/or lifestyle intervention combinations asdescribed in above, as well as relationships thereof with biologicalextractions and/or negative lifestyle habits.

In an embodiment, and still referring to FIG. 4 , a forms processingmodule 412 may sort data entered in a submission via a graphical userinterface 416 receiving expert submissions by, for instance, sortingdata from entries in the graphical user interface 416 to relatedcategories of data; for instance, data entered in an entry relating inthe graphical user interface 416 to endocrinal data may be sorted intovariables and/or data structures for endocrinal data, which may beprovided to expert endocrinal table, while data entered in an entryrelating to telomere length may be sorted into variables and/or datastructures for the storage of, telomere length data, such as experttelomeric table. Where data is chosen by an expert from pre-selectedentries such as drop-down lists, data may be stored directly; where datais entered in textual form, a language processing module 428 may be usedto map data to an appropriate existing label, for instance using avector similarity test or other synonym-sensitive language processingtest to map data to existing labels and/or categories. Similarly, datafrom an expert textual submissions 432, such as accomplished by fillingout a paper or PDF form and/or submitting narrative information, maylikewise be processed using language processing module 428.

Still referring to FIG. 4 , a language processing module 428 may includeany hardware and/or software module. Language processing module 428 maybe configured to extract, from the one or more documents, one or morewords. One or more words may include, without limitation, strings of oneor characters, including without limitation any sequence or sequences ofletters, numbers, punctuation, diacritic marks, engineering symbols,geometric dimensioning and tolerancing (GD&T) symbols, chemical symbolsand formulas, spaces, whitespace, and other symbols, including anysymbols usable as textual data as described above. Textual data may beparsed into tokens, which may include a simple word (sequence of lettersseparated by whitespace) or more generally a sequence of characters asdescribed previously. The term “token,” as used herein, refers to anysmaller, individual groupings of text from a larger source of text;tokens may be broken up by word, pair of words, sentence, or otherdelimitation. These tokens may in turn be parsed in various ways.Textual data may be parsed into words or sequences of words, which maybe considered words as well. Textual data may be parsed into “n-grams”where all sequences of n consecutive characters are considered. Any orall possible sequences of tokens or words may be stored as “chains”, forexample for use as a Markov chain or Hidden Markov Model.

Still referring to FIG. 4 , language processing module 428 may compareextracted words to categories of data to be analyzed; such data forcomparison may be entered on computing device 104 as described aboveusing expert data inputs or the like. In an embodiment, one or morecategories may be enumerated, to find total count of mentions in suchdocuments. Alternatively or additionally, language processing module 428may operate to produce a language processing model. Language processingmodel may include a program automatically generated by at least a serverand/or language processing module 428 to produce associations betweenone or more words extracted from at least a document and detectassociations, including without limitation mathematical associations,between such words, and/or associations between such words and otherelements of data analyzed, processed and/or stored by system 100.Associations between language elements, may include, without limitation,mathematical associations, including without limitation statisticalcorrelations between any language element and any other language elementand/or language elements. Statistical correlations and/or mathematicalassociations may include probabilistic formulas or relationshipsindicating, for instance, a likelihood that a given extracted wordindicates a given category of physiological data, a given relationshipof such categories to prognostic labels, and/or a given category ofprognostic labels. As a further example, statistical correlations and/ormathematical associations may include probabilistic formulas orrelationships indicating a positive and/or negative association betweenat least an extracted word and/or a given category of data; positive ornegative indication may include an indication that a given document isor is not indicating a category of data.

Still referring to FIG. 4 , language processing module 428 and/orcomputing device 104 may generate the language processing model by anysuitable method, including without limitation a natural languageprocessing classification algorithm; language processing model mayinclude a natural language process classification model that enumeratesand/or derives statistical relationships between input term and outputterms. Algorithm to generate language processing model may include astochastic gradient descent algorithm, which may include a method thatiteratively optimizes an objective function, such as an objectivefunction representing a statistical estimation of relationships betweenterms, including relationships between input terms and output terms, inthe form of a sum of relationships to be estimated. In an alternative oradditional approach, sequential tokens may be modeled as chains, servingas the observations in a Hidden Markov Model (HMM). HMMs as used hereinare statistical models with inference algorithms that that may beapplied to the models. In such models, a hidden state to be estimatedmay include an association between an extracted word category ofphysiological data, a given relationship of such categories toprognostic labels, and/or a given category of prognostic labels. Theremay be a finite number of category of physiological data, a givenrelationship of such categories to prognostic labels, and/or a givencategory of prognostic labels to which an extracted word may pertain; anHAIM inference algorithm, such as the forward-backward algorithm or theViterbi algorithm, may be used to estimate the most likely discretestate given a word or sequence of words. Language processing module 428may combine two or more approaches. For instance, and withoutlimitation, machine-learning program may use a combination ofNaive-Bayes (NB), Stochastic Gradient Descent (SGD), and parametergrid-searching classification techniques; the result may include aclassification algorithm that returns ranked associations.

Continuing to refer to FIG. 4 , generating language processing model mayinclude generating a vector space, which may be a collection of vectors,defined as a set of mathematical objects that can be added togetherunder an operation of addition following properties of associativity,commutativity, existence of an identity element, and existence of aninverse element for each vector, and can be multiplied by scalar valuesunder an operation of scalar multiplication compatible with fieldmultiplication, and that has an identity element is distributive withrespect to vector addition, and is distributive with respect to fieldaddition. Each vector in an n-dimensional vector space may berepresented by an n-tuple of numerical values. Each unique extractedword and/or language element as described above may be represented by avector of the vector space. In an embodiment, each unique extractedand/or other language element may be represented by a dimension ofvector space; as a non-limiting example, each element of a vector mayinclude a number representing an enumeration of co-occurrences of theword and/or language element represented by the vector with another wordand/or language element. Vectors may be normalized, scaled according torelative frequencies of appearance and/or file sizes. In an embodimentassociating language elements to one another as described above mayinclude computing a degree of vector similarity between a vectorrepresenting each language element and a vector representing anotherlanguage element; vector similarity may be measured according to anynorm for proximity and/or similarity of two vectors, including withoutlimitation cosine similarity, which measures the similarity of twovectors by evaluating the cosine of the angle between the vectors, whichcan be computed using a dot product of the two vectors divided by thelengths of the two vectors. Degree of similarity may include any othergeometric measure of distance between vectors.

Still referring to FIG. 4 , language processing module 428 may use acorpus of documents to generate associations between language elementsin a language processing module 428, and computing device 104 may thenuse such associations to analyze words extracted from one or moredocuments. Documents may be entered into classification device 104 bybeing uploaded by an expert or other persons using, without limitation,file transfer protocol (FTP) or other suitable methods for transmissionand/or upload of documents; alternatively or additionally, where adocument is identified by a citation, a uniform resource identifier(URI), uniform resource locator (URL) or other datum permittingunambiguous identification of the document, classification device 104may automatically obtain the document using such an identifier, forinstance by submitting a request to a database or compendium ofdocuments such as JSTOR as provided by Ithaka Harbors, Inc. of New York.

Data may be extracted from expert papers 436, which may include withoutlimitation publications in medical and/or scientific journals, bylanguage processing module 428 via any suitable process as describedherein. Persons skilled in the art, upon reviewing the entirety of thisdisclosure, will be aware of various additional methods whereby novelterms may be separated from already-classified terms and/or synonymstherefore, as consistent with this disclosure.

Referring again to FIG. 1 , computing device is configured to generate aplurality of lifestyle intervention combinations as a function ofbiological extraction using first machine-learning process 112. Amachine learning process is a process that automatedly uses a body ofdata known as “training data 124” and/or a “training set” to generate analgorithm that will be performed by a computing device/module to produceoutputs given data provided as inputs; this is in contrast to anon-machine learning software program where the commands to be executedare determined in advance by a user and written in a programminglanguage.

Still referring to FIG. 1 , training data 124, as used herein, is datacontaining correlations that a machine-learning process may use to modelrelationships between two or more categories of data elements. Forinstance, and without limitation, training data 124 may include aplurality of data entries, each entry representing a set of dataelements that were recorded, received, and/or generated together; dataelements may be correlated by shared existence in a given data entry, byproximity in a given data entry, or the like. Multiple data entries intraining data 124 may evince one or more trends in correlations betweencategories of data elements; for instance, and without limitation, ahigher value of a first data element belonging to a first category ofdata element may tend to correlate to a higher value of a second dataelement belonging to a second category of data element, indicating apossible proportional or other mathematical relationship linking valuesbelonging to the two categories. Multiple categories of data elementsmay be related in training data 124 according to various correlations;correlations may indicate causative and/or predictive links betweencategories of data elements, which may be modeled as relationships suchas mathematical relationships by machine-learning processes as describedin further detail below. Training data 124 may be formatted and/ororganized by categories of data elements, for instance by associatingdata elements with one or more descriptors corresponding to categoriesof data elements. As a non-limiting example, training data 124 mayinclude data entered in standardized forms by persons or processes, suchthat entry of a given data element in a given field in a form may bemapped to one or more descriptors of categories. Elements in trainingdata 124 may be linked to descriptors of categories by tags, tokens, orother data elements; for instance, and without limitation, training data124 may be provided in fixed-length formats, formats linking positionsof data to categories such as comma-separated value (CSV) formats and/orself-describing formats such as extensible markup language (XML),enabling processes or devices to detect categories of data.

Alternatively or additionally, and continuing to refer to FIG. 1 ,training data 124 may include one or more elements that are notcategorized; that is, training data 124 may not be formatted or containdescriptors for some elements of data. Machine-learning algorithmsand/or other processes may sort training data 124 according to one ormore categorizations using, for instance, natural language processingalgorithms, tokenization, detection of correlated values in raw data andthe like; categories may be generated using correlation and/or otherprocessing algorithms. As a non-limiting example, in a corpus of text,phrases making up a number “n” of compound words, such as nouns modifiedby other nouns, may be identified according to a statisticallysignificant prevalence of n-grams containing such words in a particularorder; such an n-gram may be categorized as an element of language suchas a “word” to be tracked similarly to single words, generating a newcategory as a result of statistical analysis. Similarly, in a data entryincluding some textual data, a person's name may be identified byreference to a list, dictionary, or other compendium of terms,permitting ad-hoc categorization by machine-learning algorithms, and/orautomated association of data in the data entry with descriptors or intoa given format. The ability to categorize data entries automatedly mayenable the same training data 124 to be made applicable for two or moredistinct machine-learning algorithms as described in further detailbelow. Training data 124 used by computing device may correlate anyinput data as described in this disclosure to any output data asdescribed in this disclosure. As a non-limiting illustrative example,training data 124 may correlate biological extraction data and/orphysiological data as inputs to lifestyle intervention combinationsand/or intervention elements. Such entries may be provided by expertentries and/or from expert database 120 s, and/or include entries byusers and/or by system 100 indicating efficacy of lifestyle interventioncombinations for persons from whom biological extractions were received.Persons skilled in the art, upon reviewing the entirety of thisdisclosure, will be aware of various elements that may be correlatedand/or included together in elements of training data 124 as consistentwith this disclosure.

Still referring to FIG. 1 , computing device 104 may be designed andconfigured to perform first machine-learning process 112 in any suitablemanner. For instance, and without limitation, computing device 104 maycreate a machine-learning model using techniques for development oflinear regression models. Linear regression models may include ordinaryleast squares regression, which aims to minimize the square of thedifference between predicted outcomes and actual outcomes according toan appropriate norm for measuring such a difference (e.g. a vector-spacedistance norm); coefficients of the resulting linear equation may bemodified to improve minimization. Linear regression models may includeridge regression methods, where the function to be minimized includesthe least-squares function plus term multiplying the square of eachcoefficient by a scalar amount to penalize large coefficients. Linearregression models may include least absolute shrinkage and selectionoperator (LASSO) models, in which ridge regression is combined withmultiplying the least-squares term by a factor of 1 divided by doublethe number of samples. Linear regression models may include a multi-tasklasso model wherein the norm applied in the least-squares term of thelasso model is the Frobenius norm amounting to the square root of thesum of squares of all terms. Linear regression models may include theelastic net model, a multi-task elastic net model, a least angleregression model, a LARS lasso model, an orthogonal matching pursuitmodel, a Bayesian regression model, a logistic regression model, astochastic gradient descent model, a perceptron model, a passiveaggressive algorithm, a robustness regression model, a Huber regressionmodel, or any other suitable model that may occur to persons skilled inthe art upon reviewing the entirety of this disclosure. Linearregression models may be generalized in an embodiment to polynomialregression models, whereby a polynomial equation (e.g. a quadratic,cubic or higher-order equation) providing a best predicted output/actualoutput fit is sought; similar methods to those described above may beapplied to minimize error functions, as will be apparent to personsskilled in the art upon reviewing the entirety of this disclosure.

Continuing to refer to FIG. 1 , machine-learning algorithms may include,without limitation, linear discriminant analysis. Machine-learningalgorithm may include quadratic discriminate analysis. Machine-learningalgorithms may include kernel ridge regression. Machine-learningalgorithms may include support vector machines, including withoutlimitation support vector classification-based regression processes.Machine-learning algorithms may include stochastic gradient descentalgorithms, including classification and regression algorithms based onstochastic gradient descent. Machine-learning algorithms may includenearest neighbors algorithms. Machine-learning algorithms may includeGaussian processes such as Gaussian Process Regression. Machine-learningalgorithms may include cross-decomposition algorithms, including partialleast squares and/or canonical correlation analysis. Machine-learningalgorithms may include naïve Bayes methods. Machine-learning algorithmsmay include algorithms based on decision trees, such as decision treeclassification or regression algorithms. Machine-learning algorithms mayinclude ensemble methods such as bagging meta-estimator, forest ofrandomized tress, AdaBoost, gradient tree boosting, and/or votingclassifier methods. Machine-learning algorithms may include neural netalgorithms, including convolutional neural net processes.

Still referring to FIG. 1 , machine-learning algorithms may includesupervised machine-learning algorithms. Supervised machine learningalgorithms, as defined herein, include algorithms that receive atraining set relating a number of inputs to a number of outputs, andseek to find one or more mathematical relations relating inputs tooutputs, where each of the one or more mathematical relations is optimalaccording to some criterion specified to the algorithm using somescoring function. For instance, a supervised learning algorithm mayinclude biological extractions and/or physiological data as describedabove as inputs, lifestyle intervention combinations and/or interventionelements as outputs, and a scoring function representing a desired formof relationship to be detected between inputs and outputs; scoringfunction may, for instance, seek to maximize the probability that agiven input and/or combination of elements inputs is associated with agiven output to minimize the probability that a given input is notassociated with a given output. Scoring function may be expressed as arisk function representing an “expected loss” of an algorithm relatinginputs to outputs, where loss is computed as an error functionrepresenting a degree to which a prediction generated by the relation isincorrect when compared to a given input-output pair provided intraining data 124. Persons skilled in the art, upon reviewing theentirety of this disclosure, will be aware of various possiblevariations of supervised machine learning algorithms that may be used todetermine relation between inputs and outputs.

Supervised machine-learning processes may include classificationalgorithms, defined as processes whereby a computing device derives,from training data 124, a model for sorting inputs into categories orbins of data. Classification may be performed using, without limitation,linear classifiers such as without limitation logistic regression and/ornaive Bayes classifiers, nearest neighbor classifiers, support vectormachines, decision trees, boosted trees, random forest classifiers,and/or neural network-based classifiers.

Still referring to FIG. 1 , machine learning processes may includeunsupervised processes. An unsupervised machine-learning process, asused herein, is a process that derives inferences in datasets withoutregard to labels; as a result, an unsupervised machine-learning processmay be free to discover any structure, relationship, and/or correlationprovided in the data. Unsupervised processes may not require a responsevariable; unsupervised processes may be used to find interestingpatterns and/or inferences between variables, to determine a degree ofcorrelation between two or more variables, or the like.

Still referring to FIG. 1 , machine-learning processes as described inthis disclosure may be used to generate machine-learning models. Amachine-learning model, as used herein, is a mathematical representationof a relationship between inputs and outputs, as generated using anymachine-learning process including without limitation any process asdescribed above, and stored in memory; an input is submitted to amachine-learning model once created, which generates an output based onthe relationship that was derived. For instance, and without limitation,a linear regression model, generated using a linear regressionalgorithm, may compute a linear combination of input data usingcoefficients derived during machine-learning processes to calculate anoutput datum. As a further non-limiting example, a machine-learningmodel may be generated by creating an artificial neural network, such asa convolutional neural network comprising an input layer of nodes, oneor more intermediate layers, and an output layer of nodes. Connectionsbetween nodes may be created via the process of “training” the network,in which elements from a training data 124 set are applied to the inputnodes, a suitable training algorithm (such as Levenberg-Marquardt,conjugate gradient, simulated annealing, or other algorithms) is thenused to adjust the connections and weights between nodes in adjacentlayers of the neural network to produce the desired values at the outputnodes. This process is sometimes referred to as deep learning.

A machine-learning process may include a lazy-learning process. Alazy-learning process and/or protocol, which may alternatively bereferred to as a “lazy loading” or “call-when-needed” process and/orprotocol, may be a process whereby machine learning is conducted uponreceipt of an input to be converted to an output, by combining the inputand training set to derive the algorithm to be used to produce theoutput on demand. For instance, an initial set of simulations may beperformed to cover an initial heuristic and/or “first guess” at anoutput and/or relationship. As a non-limiting example, an initialheuristic may include a ranking of associations between inputs andelements of training data 124. Heuristic may include selecting somenumber of highest-ranking associations and/or training data 124elements. Lazy learning may implement any suitable lazy learningalgorithm, including without limitation a K-nearest neighbors algorithm,a lazy naïve Bayes algorithm, or the like; persons skilled in the art,upon reviewing the entirety of this disclosure, will be aware of variouslazy-learning algorithms that may be applied to generate outputs asdescribed in this disclosure, including without limitation lazy learningapplications of machine-learning algorithms as described in furtherdetail below.

With continued reference to FIG. 1 , and as a non-limiting example,generating plurality of lifestyle intervention combinations may includeperforming a machine-learning process that generates lifestyleintervention combinations and/or intervention elements from biologicalextraction data; for instance, and without limitation, computing devicemay generate a machine-learning model using first machine-learningprocess 112 and training data 124 having entries correlating biologicalextract data and/or physiological data with lifestyle interventioncombinations and/or intervention elements, producing a machine-learningmodel that inputs biological extraction and outputs a plurality oflifestyle intervention combinations and/or intervention elements.

Still referring to FIG. 1 , generating the plurality of lifestyleintervention combinations may alternatively or additionally includereceiving a description of at least a current disease state of the userand generating the plurality of lifestyle intervention combinations as afunction of the current disease state. As used in this disclosure a“current disease state” includes any current, nascent, and/or probablefuture medical condition based on biological extractions, for instanceas may be denoted by a prognostic label as described in U.S.Nonprovisional application Ser. No. 16/372,512, dated Apr. 2, 2019, andentitled “METHODS AND SYSTEMS FOR UTILIZING DIAGNOSTICS FOR INFORMEDVIBRANT CONSTITUTIONAL GUIDANCE,” The entirety of which is incorporatedby reference in this disclosure. Identification of a current diseasestate may be received from a device operated by user, such as withoutlimitation a personal computer, mobile device, or the like.Alternatively or additionally, identification of a current disease statemay be received from a third-party device such as from a doctor or othermedical professional, family member, friend, partner, or the like. In afurther non-limiting example, current disease state may be determinedfrom biological extraction using database lookup and/or machine-learningmethods, for instance and without limitation as described in U.S.Nonprovisional application Ser. No. 16/372,512.

Still referring to FIG. 1 , generation of lifestyle interventioncombinations using current disease state may include generating amachine-learning model and/or executing a machine learning process thattakes current disease state as an input and outputs a plurality oflifestyle intervention combinations and/or intervention elements, forinstance as described in U.S. Nonprovisional application Ser. No.16/372,512.

Further referring to FIG. 1 , generating the plurality of lifestyleintervention combinations may include identifying a negative lifestylebehavior of the user. A negative lifestyle behavior may include a habitand/or behavior a user is engaged in that tends to act to the detrimentof the user's health. A habit a user is engaged in may be a nutritionalhabit, such as a daily consumption of sugar, fat, fiber, protein, or thelike. A habit a user is engaged in may include an exercise habit, whichmay be measured in terms of a duration per day, week, or the like ofcardiovascular exercise, resistance training exercise, or other exercisecategory, a number of steps per week taken, resting and/or total calorieconsumption numbers, or the like. A habit a user is engaged in mayinclude a substance abuse habit, including some measure of a dosage perperiod of time consumed of a harmful and/or addictive substance such asan opiate, alcohol, tobacco, stimulants such as cocaine, methamphetamineor the like, hallucinogens, narcotics, or other mood-altering chemicals.A habit a user is engaged in may include a sleep habit, including anumber of hours per night a user sleeps, a number of nights a user goeswith less than a recommended amount of sleep, or the like.

Still referring to FIG. 1 , computing device 104 may receive informationidentifying a negative lifestyle behavior from a device operated byuser; for instance, user may provide the input after a lapse inself-control. Negative lifestyle behavior may be identified by a userentry; for instance, and without limitation, computing device 104 mayprovide a user with a questionnaire in the form of one or more datafields requesting that the user identify activities in which the userengaged. Questions presented to a user may include a number of servingsof alcohol a user consumes during a given period of time such as a day,a week or a year, a quantity of tobacco, drugs, or other substances thata user consumes during a given period of time, a number of hours a usersleeps in a night, or the like. A user may respond to such questions byselecting options corresponding to particular ranges of data, by settingsliders or other indicators of a quantity along a continuous range, byentering values in drop-down lists, and/or by typing in numbers or text.Alternatively or additionally, another person, potentially from adifferent remote device, may report that user has engaged in thenegative lifestyle behavior. For instance, a family member, neighbor,spouse, boyfriend, girlfriend, ex-boyfriend, ex-girlfriend, religiousleader, co-worker, or the like may observe user engaging in negativelifestyle behavior, such as a drinking binge, a pattern of overeating, atendency to sessile behavior, or the like. Computing device 104 maytrack such notifications and/or compare such notifications to negativebehavioral propensities. For instance, computing device 104 may record afirst such report as indicative that user is at an elevated risk toengage in negative lifestyle behavior. In an embodiment, one or morewords and/or phrases entered by a user, who may include any user asdescribed above, may be mapped to a label, or particular word or phraseused by computing device 104 to describe an object, behavior, negativelifestyle behavior, negative behavioral tendency, or the like, using alanguage processing model, module, and/or algorithm as described above;for instance, computing device 104 may determine using a languageprocessing model, module, and/or algorithm as described above that theword or phrase entered by the user is a synonym of the label, and maysubstitute the label for the word or phrase.

Alternatively or additionally, identifying negative lifestyle behaviormay include receiving a training set correlating biological extractionsto negative lifestyle behaviors, generating negative behavior identifiermodel using a supervised machine-learning algorithm and the trainingset, producing a negative lifestyle behavior output from the negativebehavior identifier model using the biological extraction, andidentifying the negative lifestyle behavior as a function of thenegative behavior output. Training set may correlate biologicalextractions to negative lifestyle behaviors by matching biologicalextraction data of individual who self-report particular negativehabits; training set entries may alternatively or additionally beprovided by expert submissions as described above. Identifying anegative lifestyle behavior may include generating a negative lifestylebehavior identifier model using a supervised machine-learning algorithmand the training set; negative habit identifier model may be generated,without limitation, using a classification algorithm, so that negativehabit identifier may match biological extraction data to a most likelyhabit or a set of most likely habits of which user may partake.Identifying a negative lifestyle behavior may include producing anegative habit output from the negative habit identifier model usingbiological extraction data and identifying the negative lifestylebehavior as a function of the negative habit output.

Still referring to FIG. 1 , classification algorithm used as above toidentify a negative lifestyle behavior may generate a classifier. A“classifier,” as used in this disclosure is a machine-learning model,such as a mathematical model, neural net, or program generated by amachine learning algorithm known as a “classification algorithm,” asdescribed in further detail below, that sorts inputs into categories orbins of data, outputting the categories or bins of data and/or labelsassociated therewith. A classifier may be configured to output at leasta datum that labels or otherwise identifies a set of data that areclustered together, found to be close under a distance metric asdescribed below, or the like. Computing device 104 and/or another devicemay generate a classifier using a classification algorithm, defined as aprocesses whereby a computing device 104 derives a classifier fromtraining data 124. Classification may be performed using, withoutlimitation, linear classifiers such as without limitation logisticregression and/or naive Bayes classifiers, nearest neighbor classifierssuch as k-nearest neighbors classifiers, support vector machines, leastsquares support vector machines, fisher's linear discriminant, quadraticclassifiers, decision trees, boosted trees, random forest classifiers,learning vector quantization, and/or neural network-based classifiers.

Still referring to FIG. 1 , computing device 104 may be configured togenerate a classifier using a Naïve Bayes classification algorithm.Naïve Bayes classification algorithm generates classifiers by assigningclass labels to problem instances, represented as vectors of elementvalues. Class labels are drawn from a finite set. Naïve Bayesclassification algorithm may include generating a family of algorithmsthat assume that the value of a particular element is independent of thevalue of any other element, given a class variable. Naïve Bayesclassification algorithm may be based on Bayes Theorem expressed asP(A/B)=P(B/A) P(A)÷P(B), where P(AB) is the probability of hypothesis Agiven data B also known as posterior probability; P(B/A) is theprobability of data B given that the hypothesis A was true; P(A) is theprobability of hypothesis A being true regardless of data also known asprior probability of A; and P(B) is the probability of the dataregardless of the hypothesis. A naïve Bayes algorithm may be generatedby first transforming training data 124 into a frequency table.Computing device 104 may then calculate a likelihood table bycalculating probabilities of different data entries and classificationlabels. Computing device 104 may utilize a naïve Bayes equation tocalculate a posterior probability for each class. A class containing thehighest posterior probability is the outcome of prediction. Naïve Bayesclassification algorithm may include a gaussian model that follows anormal distribution. Naïve Bayes classification algorithm may include amultinomial model that is used for discrete counts. Naïve Bayesclassification algorithm may include a Bernoulli model that may beutilized when vectors are binary.

With continued reference to FIG. 1 , computing device 104 may beconfigured to generate a classifier using a K-nearest neighbors (KNN)algorithm. A “K-nearest neighbors algorithm” as used in this disclosure,includes a classification method that utilizes feature similarity toanalyze how closely out-of-sample-features resemble training data 124 toclassify input data to one or more clusters and/or categories offeatures as represented in training data 124; this may be performed byrepresenting both training data 124 and input data in vector forms, andusing one or more measures of vector similarity to identifyclassifications within training data 124, and to determine aclassification of input data. K-nearest neighbors algorithm may includespecifying a K-value, or a number directing the classifier to select thek most similar entries training data 124 to a given sample, determiningthe most common classifier of the entries in the database, andclassifying the known sample; this may be performed recursively and/oriteratively to generate a classifier that may be used to classify inputdata as further samples. For instance, an initial set of samples may beperformed to cover an initial heuristic and/or “first guess” at anoutput and/or relationship, which may be seeded, without limitation,using expert input received according to any process as describedherein. As a non-limiting example, an initial heuristic may include aranking of associations between inputs and elements of training data124. Heuristic may include selecting some number of highest-rankingassociations and/or training data 124 elements.

With continued reference to FIG. 1 , generating k-nearest neighborsalgorithm may generate a first vector output containing a data entrycluster, generating a second vector output containing an input data, andcalculate the distance between the first vector output and the secondvector output using any suitable norm such as cosine similarity,Euclidean distance measurement, or the like. Each vector output may berepresented, without limitation, as an n-tuple of values, where n is atleast two values. Each value of n-tuple of values may represent ameasurement or other quantitative value associated with a given categoryof data, or attribute, examples of which are provided in further detailbelow; a vector may be represented, without limitation, in n-dimensionalspace using an axis per category of value represented in n-tuple ofvalues, such that a vector has a geometric direction characterizing therelative quantities of attributes in the n-tuple as compared to eachother. Two vectors may be considered equivalent where their directions,and/or the relative quantities of values within each vector as comparedto each other, are the same; thus, as a non-limiting example, a vectorrepresented as [5, 10, 15] may be treated as equivalent, for purposes ofthis disclosure, as a vector represented as [1, 2, 3]. Vectors may bemore similar where their directions are more similar, and more differentwhere their directions are more divergent; however, vector similaritymay alternatively or additionally be determined using averages ofsimilarities between like attributes, or any other measure of similaritysuitable for any n-tuple of values, or aggregation of numericalsimilarity measures for the purposes of loss functions as described infurther detail below. Any vectors as described herein may be scaled,such that each vector represents each attribute along an equivalentscale of values. Each vector may be “normalized,” or divided by a“length” attribute, such as a length attribute l as derived using aPythagorean norm: l=√{square root over (Σ_(i=0) ^(n)a_(i) ²)}, wherea_(i) is attribute number i of the vector. Scaling and/or normalizationmay function to make vector comparison independent of absolutequantities of attributes, while preserving any dependency on similarityof attributes; this may, for instance, be advantageous where casesrepresented in training data 124 are represented by different quantitiesof samples, which may result in proportionally equivalent vectors withdivergent values.

With continued reference to FIG. 1 , computing device 104 may generate aplurality of lifestyle intervention combinations as a function of and/orbased on the negative lifestyle behavior. Each lifestyle interventioncombination may alleviate negative lifestyle behavior; in other words,each lifestyle intervention may include intervention components tendingto counteract negative effects of negative lifestyle behavior, either byreducing and/or ceasing the negative lifestyle behavior and/or by actingto reduce negative health effects thereof. For example, alleviation of anegative lifestyle behavior including a sedentary lifestyle may includean exercise program and/or a dietary change reducing caloric intake toreduce risk of weight gain. As a further example alleviation of anegative lifestyle behavior including overconsumption of simple sugarsmay include an instruction to consume fewer sugars, for instance byusing sugar substitutes, and/or may recommend an increase in exerciseand/or consumption of foods tending to lower glycemic index to reducethe impact of the additional sugar on the body.

Still referring to FIG. 1 , computing device 104 may perform one or moremachine-learning processes, as described above, to generate a pluralityof lifestyle intervention combinations based on negative lifestylebehavior. For instance, and without limitation, first machine-learningprocess 112 may include a first process and/or model generating negativelifestyle behavior from biological extraction data and a second processand/or model generating lifestyle intervention combinations as afunction of negative lifestyle behavior; the latter may be trained,without limitation, using training data 124 having entries correlatingnegative lifestyle behaviors and lifestyle interventions and/orintervention elements tending to alleviate negative lifestyle behaviors,which may be assembled, received, and/or aggregated using any methodsabove, including without limitation user entries, case histories, and/orexpert submissions. As a further non-limiting example, firstmachine-learning process 112 may include a machine learning processgenerating lifestyle intervention combinations and/or interventionelements from combinations of biological extraction data negativelifestyle behavior data; the latter may be trained, without limitation,using training data 124 having entries correlating combinations negativelifestyle behaviors and biological extraction with lifestyleinterventions and/or intervention elements tending to alleviate negativelifestyle behaviors and/or conditions and/or risks associated withbiological extractions such as biomarkers indicative of elevated heartdisease risk or the like, which may be assembled, received, and/oraggregated using any methods above, including without limitation userentries, case histories, and/or expert submissions. As a furthernon-limiting example, first machine-learning process 112 generatelifestyle intervention combinations and/or intervention elements frombiological extraction data and a second process and/or model maylifestyle intervention combinations as a function of negative lifestylebehavior, where the latter may be trained, without limitation, usingtraining data 124 having entries correlating negative lifestylebehaviors and lifestyle interventions and/or intervention elementstending to alleviate negative lifestyle behaviors, which may beassembled, received, and/or aggregated using any methods above,including without limitation user entries, case histories, and/or expertsubmissions; the output of the first machine-learning process 112 andsecond machine-learning process 128 may be combined to produce anaggregate output, which may, for instance, be made up of theintersection of the output of the first machine-learning process 112 andthe second machine-learning process 128, a combination of all elementsof both processes, or the like, any of which aggregate outputs may beranked according to any process provided herein for ranking results.

With continued reference to FIG. 1 , results as described above relatingto negative lifestyle behaviors may be combined in any manner describedabove with results relating to current disease states, for instance byaggregation as described above with such results and/or by performanceof one or more machine-learning processes, as described above, usingtraining data 124 relating any combination of biological extractiondata, current disease state data, and/or negative lifestyle behaviordata to any combination of lifestyle intervention combinations,intervention elements, current disease states, and/or negative lifestylebehaviors.

Still referring to FIG. 1 , computing device 104 is configured toassign, to each lifestyle intervention combination of plurality oflifestyle intervention combinations, a degree of projected useradherence to the lifestyle intervention combination, using a secondmachine-learning process 128. A “degree of adherence,” as used in thisdisclosure, is a degree to which a user performs a recommended lifestyleintervention combination; a “projected degree of adherence” is anestimated degree of adherence to a lifestyle intervention combination inthe future. A degree of adherence and/or projected degree of adherencemay include a quantitative measure such as a score, a percentage ofadherence, or the like. Assigning includes performing a second machinelearning process, which may include any machine-learning process asdescribed above.

In an embodiment, and still referring to FIG. 1 , assigning the degreeof projected user adherence may include providing a user inclinationenumeration, determining, using a classifier, a distance from the userinclination enumeration to each lifestyle intervention combination ofthe plurality of lifestyle intervention combinations, and assigning thedegree of projected user adherence using the distance. A “userinclination enumeration,” as used in this disclosure, is a datastructure that represents a quantitative measure of a degree ofimportance a user places on each of a plurality of interventionelements; quantitative measures may be used to express both fondness forand aversion to intervention elements, such as without limitation bydescribing aversion with negative values and fondness with positivevalues, or any suitable alternative approach therefor that may occur toa person skilled in the art upon reviewing the entirety of thisdisclosure. A user inclination enumeration may include an n-tuple ofvalues, where n is at least two values, as described in further detailbelow. Each value of n-tuple of values may represent a measurement orother quantitative value associated with a given category of data, orattribute, examples of which are provided in further detail below;n-tuple may be represented, without limitation, as a vector inn-dimensional space using an axis per category of value represented inn-tuple of values, such that a vector has a geometric directioncharacterizing the relative quantities of attributes in the n-tuple ascompared to each other. Two vectors may be considered equivalent wheretheir directions, and/or the relative quantities of values within eachvector as compared to each other, are the same; thus, as a non-limitingexample, a vector represented as [5, 10, 15] may be treated asequivalent, for purposes of this disclosure, as a vector represented as[1, 2, 3]. Vectors may be more similar where their directions are moresimilar, and more different where their directions are more divergent;however, vector similarity may alternatively or additionally bedetermined using averages of similarities between like attributes, orany other measure of similarity suitable for any n-tuple of values, oraggregation of numerical similarity measures for the purposes of lossfunctions as described in further detail below. Any vectors as describedherein may be scaled, such that each vector represents each attributealong an equivalent scale of values. Each vector may be “normalized,” ordivided by a “length” attribute, such as a length attribute/as derivedusing a Pythagorean norm: l=√{square root over (Σ_(i=0) ^(n)a_(i) ²)},where a_(i) is attribute number i of the vector. Scaling and/ornormalization may function to make vector comparison independent ofabsolute quantities of attributes, while preserving any dependency onsimilarity of attributes; this may, for instance be advantageous whereeach vector represents a weighing of user priorities, and/or is to becompared to such a weighing of user inclinations.

Continuing to refer to FIG. 1 , user inclination enumeration may includea plurality of entries, which are attributes of user inclinationenumeration as described above. Entries may include, without limitation,an attribute indicating a degree of importance to user of cost of anaction that may be taken to improve health, alleviate a current diseasestate, and/or mitigate effects of a negative lifestyle behavior, such asan intervention and/or intervention element as described above; forinstance, an entry may indicate a strong aversion to high-intensityexercise and/or one or more categories thereof, while another entry mayindicate a slight fondness for low-intensity exercise such as walking oryoga, or the like. As another non-limiting example, an entry mayindicate an aversion to a given kind of food and/or dietary elimination;for instance a user may be averse to removal of carbohydrates from theuser's diet, while being relatively accepting of a portion-controlprotocol and/or an increase in consumption of vegetables.

Referring again to FIG. 1 , computing device 104 may derive userinclination enumeration by receiving at least a user input. Forinstance, a graphical user interface 416 may display at user deviceoptions to rate one or more priorities absolutely and/or relatively toeach other, for instance by providing a numerical rating scale withradio buttons and/or drop-down lists, sliders where a user may setrelative importance along a continuum for each user inclinationenumeration attribute, and/or textual entry fields wherein a user mayenter numbers reflecting user's personal degree of fondness and/oraversion for an intervention element corresponding to each field.

Alternatively or additionally, and still referring to FIG. 1 , derivingthe user inclination enumeration may include generating a defaultenumeration; a default enumeration may contain default values thatrepresent a “first guess” by system 100 for what user's relativepriorities, likes, and/or dislikes are likely to be. Default enumerationmay be stored in and/or retrieved from expert database 120, which may bepopulated based on an expert determination of likely priorities.Alternatively, a person acquainted with user may enter, in a display asdescribed above, what that person believes user's priorities are likelyto be; multiple such entries may be aggregated, averaged, or the like.In an embodiment, computing device 104 may use a machine-learningprocess to generate a default enumeration; this may be performed bypredicting a user's likely priorities and/or preferences based onpreviously determined priorities and/or preferences of another person.For instance, generating a default enumeration may include receiving adefault enumeration training set correlating a cohort of individualinformation to individual user inclination enumerations. Defaultenumeration training set may include a plurality of entries, each entrycorresponding to a different person; entries may be anonymized topreserve individual privacy. Each entry of plurality of entries mayinclude a set of personal data, pertaining to a person represented bythe entry, which may include any information suitable for inclusion inuser database 108 as described above, including user preferences,habits, health information including without limitation biologicalextraction data, negative lifestyle behavior data, user demographicdata, and the like. Each entry may also include a user inclinationenumeration, which may include any element and/or elements suitable forinclusion in user inclination enumeration as described above.

Still referring to FIG. 1 , computing device 104 may generating a set ofuser data regarding the user; set of user data may be generated to matchcategories of data in entries in default enumeration training set. In anembodiment set of user data may be generated by querying user database108. Alternatively or additionally, one or more elements of set of userdata may be obtained by prompting user to enter the one or more elementsat a user device and receiving the one or more elements in response tothe prompting; one or more elements may be obtained, alternatively oradditionally, by prompting another person, for instance at or via anadditional client device, to provide the one or more elements of data,and receiving the one or more elements in response. The above-describedmethods may be combined; for instance, computing device 104 may queryuser database 108 to obtain some elements of user data, determine thatone or more elements matching categories in default enumeration databaseare missing, and prompt user and/or another person to provide suchelements. Persons skilled in the art, upon reviewing the entirety ofthis disclosure, will be aware of various ways in which user data may becollected and/or generated consistently with this disclosure.

With continued reference to FIG. 1 , computing device 104 may derivedefault enumeration from training set as a function of set of user data,using any suitable machine learning algorithm. As a non-limitingexample, computing device 104 may derive default enumeration fromtraining set using a lazy-learning process, and/or classificationprocess, which may include any lazy-learning process and/orclassification process as described above, including without limitationa K-nearest neighbors algorithm; K-nearest neighbors may return a singlematching entry, or a plurality of matching entries. Where a plurality ofmatching entries are returned, computing device 104 may derive defaultenumeration from plurality of matching entries by aggregating userinclination enumerations of matching entries; aggregation may beperformed using any suitable method for aggregation, includingcomponent-wise addition followed by normalization, component-wisecalculation of arithmetic means, or the like. Persons skilled in theart, upon reviewing the entirety of this disclosure, will be aware ofvarious ways in which multiple user inclination enumerations may becombined to create a default enumeration.

Still referring to FIG. 1 , deriving the user inclination enumerationmay additionally include displaying a default enumeration to the user.Default enumeration may be displayed to user via a user device. In anembodiment, display of default enumeration to user may be performed bypopulating data entry fields usable for user to enter values of userhealth vector with values taken from default enumeration. Such populateddata entry fields may be displayed to user, indicating a first guess atuser's likely preferences. Computing device 104 may receive a usercommand modifying the default enumeration; command may be received inthe form of a modification and/or replacement by user of a valuedisplayed in a user entry field. Computing device 104 may derive userinclination enumeration using the default enumeration and the usercommand; for instance, and without limitation, system may adopt usermodifications to default enumeration to produce a user inclinationenumeration.

Still referring to FIG. 1 , user inclination enumeration may be storedin memory of computing device 104, including without limitation in userdatabase 108 as described above. User inclination enumeration may beupdated periodically; for instance a user may modify user inclinationenumeration via a user interface, for instance to change one or morerelative priorities to match user inclination enumeration. User mayenter a command to view user inclination enumeration, modify one or moreparameters and/or attributes of user inclination enumeration, and causecomputing device 104 to store modified at least a user inclinationenumeration.

With continuing reference to FIG. 1 , assigning a degree of projecteduser adherence may include receiving a training set correlating a setsof individual user inclination enumerations and lifestyle interventioncombinations with adherence data, where “adherence data” is defined forthis purpose as data indicating a degree to which a user having a givenuser inclination enumeration adhered with a given lifestyle interventioncombination; each element of adherence data may include an adherencescore and/or other quantification of a degree to which a user adhered tothe lifestyle intervention combination. Such elements of training data124 may be received in any manner suitable for receipt of elements oftraining data 124 as described above, including without limitationexpert inputs and/or user reported data. Computing device 104 maygenerate, using training set, an adherence score for each lifestyleintervention combination of plurality of lifestyle combinations as afunction of a user inclination enumeration associated with the user.This may be performed using classification algorithms and/orclassifiers, for instance and without limitation by representinglifestyle combinations and user inclination enumerations as vectorshaving corresponding attributes and determining a degree of proximitysuch as without limitation any measure of proximity as described above.Alternatively, training data 124 may be used to generate amachine-learning model, such as without limitation a regression model orother model representing a mathematical expression of training data 124elements, which model outputs an adherence score quantifying degree ofuser adherence. However calculated, computing device 104 may assigndegree of projected user adherence as a function of and/or usingadherence score.

Still referring to FIG. 1 , computing device 104 may alternatively oradditionally assign a degree of projected user adherence by receiving atraining set correlating a sets of user data and lifestyle interventioncombinations with adherence data; training set may be generated usingany process and/or process step described above, including withoutlimitation user inputs and/or expert entries. Computing device 104 maygenerate, using the training set, an adherence score for each lifestyleintervention combination of the plurality of lifestyle combinations as afunction of user data associated with the user; this may be performed asbefore using a classification algorithm and/or distance metriccalculation, and/or using a machine-learning model generated as afunction of the training data 124. User data may be received from adevice operated by user and/or by querying user database 108. Computingdevice 104 may assign degree of projected user adherence as a functionof the adherence score.

With continued reference to FIG. 1 , computing device 104 may beconfigured to select a lifestyle intervention from the plurality oflifestyle intervention combinations as a function of the degree ofprojected user adherence of the selected lifestyle interventioncombination. For instance, and without limitation, computing device 104may select a lifestyle intervention combination having a maximal degreeof projected user adherence. Alternatively or additionally, computingdevice 104 may rank intervention combinations according to projecteddegrees of user adherence and present two or more of the plurality ofintervention combinations to user and/or via a computing device operatedby user; user may select an intervention combination from the presentedlist of interventions and/or computing device 104 may receive a userselection of an intervention combination of the presented interventioncombinations.

In an embodiment, and still referring to FIG. 1 , before or afterselection of a lifestyle intervention combination having a maximaldegree of projected user adherence, computing device 104 may filterplurality of lifestyle intervention combinations by removing one or morelifestyle intervention combinations from plurality of lifestyleintervention combinations. This may be accomplished, without limitation,by receiving a user proscription, defined for purposes of thisdisclosure as any data indicating that an intervention element and/orlifestyle intervention combination is unsuitable for a user, identifyinga lifestyle intervention combination of the plurality of lifestyleintervention combinations that conflicts with the user proscription, andeliminating the identified lifestyle intervention combination from theplurality of lifestyle intervention combinations. Identifying thatlifestyle intervention combination conflicts with the user proscriptionmay include identifying that the lifestyle intervention combinationincludes one or more intervention elements and/or combinations thereofthat are forbidden by user proscription. User proscription may include adietary restriction, allergy, food sensitivity, genetic condition suchas phenylketonuria, or the like that prevents consumption of certainfoods and/or classes of foods, an injury that prevents user fromperforming an exercise and/or class of exercises, or any othercounterindication and/or user restriction as defined in this disclosureand/or in disclosures incorporated herein by reference. Userproscription may include a user belief proscription, which is definedfor purposes herein as a user proscription that prevents a user fromengaging in a lifestyle intervention combination and/or interventionelement because of religious and/or personal beliefs, such as withoutlimitation dietary prohibitions imposed by kosher and/or halal beliefsystems and/or religious rules. Computing device 104 may be configuredto identify a lifestyle intervention combination of the plurality oflifestyle intervention combinations that conflicts with the user beliefproscription and eliminate the identified lifestyle interventioncombination from the plurality of lifestyle intervention combinations.As noted above, each step of each process may be performed repeatedlyand/or iteratively; for instance, one or more steps may be performed aspart of a feedback loop wherein user activity and/or biologicalextractions are recorded, compared to lifestyle interventioncombinations, and/or compared to previous biological extractions, andsubsequent performance of any steps described in this description basedon modified biological extraction and/or user activity information.

Referring now to FIG. 5 , an exemplary embodiment of a method 500 ofgenerating lifestyle change recommendations based on biologicalextractions is presented. At step 505, receiving, by a computing device,a biological extraction pertaining to a user; this may be performed,without limitation, as described above in reference to FIGS. 1-4 .

Still referring at step 510, generating, by the computing device, andusing a first machine-learning process 112, a plurality of lifestyleintervention combinations as a function of the biological extraction;this may be performed, without limitation, as described above inreference to FIGS. 1-4 . Generating plurality of lifestyle interventioncombinations may include receiving a description of at least a currentdisease state of the user and generating the plurality of lifestyleintervention combinations as a function of the current disease state.Generating plurality of lifestyle intervention combinations may includeidentifying a negative lifestyle behavior of the user and generating aplurality of lifestyle intervention combinations, wherein each lifestyleintervention combination alleviates the negative lifestyle behavior.Identifying the negative lifestyle behavior may include receiving atraining set correlating biological extractions to negative lifestylebehaviors, generating a negative behavior identifier model using asupervised machine-learning algorithm and the training set, producing anegative lifestyle behavior output from the negative behavior identifiermodel using the biological extraction, and identifying the negativelifestyle behavior as a function of the negative behavior output.

At step 515, assigning, by a computing device and to each lifestyleintervention combination of the plurality of lifestyle interventioncombinations, a degree of projected user adherence to the lifestyleintervention combination, wherein assigning further comprises performinga second machine learning process; this may be performed, withoutlimitation, as described above in reference to FIGS. 1-4 . Assigningdegree of projected user adherence may include providing a userinclination enumeration, determining, using a classifier, a distancefrom the user inclination enumeration to each lifestyle interventioncombination of the plurality of lifestyle intervention combinations, andassigning the degree of projected user adherence using the distance.Providing user inclination enumeration may include generating a defaultenumeration, displaying the default enumeration to the user, receiving auser command modifying the default enumeration, and deriving the userinclination enumeration using the default enumeration and the usercommand. Generating default enumeration may include receiving a trainingset correlating a cohort of individual information to individual userinclination enumerations, retrieving a set of user data regarding theuser, and deriving the default enumeration from the training set as afunction of the set of user data using a classification process. Thismay include receiving a training set correlating a sets of individualuser inclination enumerations and lifestyle intervention combinationswith adherence data generating, using the training set, an adherencescore for each lifestyle intervention combination of the plurality oflifestyle combinations as a function of the user inclinationenumeration, and assigning the degree of projected user adherence as afunction of the adherence score. Assigning degree of projected useradherence may include receiving a training set correlating a sets ofuser data and lifestyle intervention combinations with adherence dataand generating, using the training set, an adherence score for eachlifestyle intervention combination of the plurality of lifestylecombinations as a function of user data of the user, and assigning thedegree of projected user adherence as a function of the adherence score.

At step 520, and still referring to FIG. 5 , computing device 104 mayselect, from the plurality of lifestyle intervention combinations, alifestyle intervention combination as a function of the degree ofprojected user adherence to the selected lifestyle interventioncombination; this may be performed, without limitation, as describedabove in reference to FIGS. 1-4 . Computing device 104 may receive auser belief proscription, identify a lifestyle intervention combinationof the plurality of lifestyle intervention combinations that conflictswith the user belief proscription, and eliminate the identifiedlifestyle intervention combination from the plurality of lifestyleintervention combinations.

It is to be noted that any one or more of the aspects and embodimentsdescribed herein may be conveniently implemented using one or moremachines (e.g., one or more computing devices that are utilized as auser computing device for an electronic document, one or more serverdevices, such as a document server, etc.) programmed according to theteachings of the present specification, as will be apparent to those ofordinary skill in the computer art. Appropriate software coding canreadily be prepared by skilled programmers based on the teachings of thepresent disclosure, as will be apparent to those of ordinary skill inthe software art. Aspects and implementations discussed above employingsoftware and/or software modules may also include appropriate hardwarefor assisting in the implementation of the machine executableinstructions of the software and/or software module.

Such software may be a computer program product that employs amachine-readable storage medium. A machine-readable storage medium maybe any medium that is capable of storing and/or encoding a sequence ofinstructions for execution by a machine (e.g., a computing device) andthat causes the machine to perform any one of the methodologies and/orembodiments described herein. Examples of a machine-readable storagemedium include, but are not limited to, a magnetic disk, an optical disc(e.g., CD, CD-R, DVD, DVD-R, etc.), a magneto-optical disk, a read-onlymemory “ROM” device, a random access memory “RAM” device, a magneticcard, an optical card, a solid-state memory device, an EPROM, an EEPROM,and any combinations thereof. A machine-readable medium, as used herein,is intended to include a single medium as well as a collection ofphysically separate media, such as, for example, a collection of compactdiscs or one or more hard disk drives in combination with a computermemory. As used herein, a machine-readable storage medium does notinclude transitory forms of signal transmission.

Such software may also include information (e.g., data) carried as adata signal on a data carrier, such as a carrier wave. For example,machine-executable information may be included as a data-carrying signalembodied in a data carrier in which the signal encodes a sequence ofinstruction, or portion thereof, for execution by a machine (e.g., acomputing device) and any related information (e.g., data structures anddata) that causes the machine to perform any one of the methodologiesand/or embodiments described herein.

Examples of a computing device include, but are not limited to, anelectronic book reading device, a computer workstation, a terminalcomputer, a server computer, a handheld device (e.g., a tablet computer,a smartphone, etc.), a web appliance, a network router, a networkswitch, a network bridge, any machine capable of executing a sequence ofinstructions that specify an action to be taken by that machine, and anycombinations thereof. In one example, a computing device may includeand/or be included in a kiosk.

FIG. 6 shows a diagrammatic representation of one embodiment of acomputing device in the exemplary form of a computer system 600 withinwhich a set of instructions for causing a control system to perform anyone or more of the aspects and/or methodologies of the presentdisclosure may be executed. It is also contemplated that multiplecomputing devices may be utilized to implement a specially configuredset of instructions for causing one or more of the devices to performany one or more of the aspects and/or methodologies of the presentdisclosure. Computer system 600 includes a processor 604 and a memory608 that communicate with each other, and with other components, via abus 612. Bus 612 may include any of several types of bus structuresincluding, but not limited to, a memory bus, a memory controller, aperipheral bus, a local bus, and any combinations thereof, using any ofa variety of bus architectures.

Processor 604 may include any suitable processor, such as withoutlimitation a processor incorporating logical circuitry for performingarithmetic and logical operations, such as an arithmetic and logic unit(ALU), which may be regulated with a state machine and directed byoperational inputs from memory and/or sensors; processor 604 may beorganized according to Von Neumann and/or Harvard architecture as anon-limiting example. Processor 604 may include, incorporate, and/or beincorporated in, without limitation, a microcontroller, microprocessor,digital signal processor (DSP), Field Programmable Gate Array (FPGA),Complex Programmable Logic Device (CPLD), Graphical Processing Unit(GPU), general purpose GPU, Tensor Processing Unit (TPU), analog ormixed signal processor, Trusted Platform Module (TPM), a floating pointunit (FPU), and/or system on a chip (SoC)

Memory 608 may include various components (e.g., machine-readable media)including, but not limited to, a random-access memory component, a readonly component, and any combinations thereof. In one example, a basicinput/output system 616 (BIOS), including basic routines that help totransfer information between elements within computer system 600, suchas during start-up, may be stored in memory 608. Memory 608 may alsoinclude (e.g., stored on one or more machine-readable media)instructions (e.g., software) 620 embodying any one or more of theaspects and/or methodologies of the present disclosure. In anotherexample, memory 608 may further include any number of program modulesincluding, but not limited to, an operating system, one or moreapplication programs, other program modules, program data, and anycombinations thereof.

Computer system 600 may also include a storage device 624. Examples of astorage device (e.g., storage device 624) include, but are not limitedto, a hard disk drive, a magnetic disk drive, an optical disc drive incombination with an optical medium, a solid-state memory device, and anycombinations thereof. Storage device 624 may be connected to bus 612 byan appropriate interface (not shown). Example interfaces include, butare not limited to, SCSI, advanced technology attachment (ATA), serialATA, universal serial bus (USB), IEEE 1394 (FIREWIRE), and anycombinations thereof. In one example, storage device 624 (or one or morecomponents thereof) may be removably interfaced with computer system 600(e.g., via an external port connector (not shown)). Particularly,storage device 624 and an associated machine-readable medium 628 mayprovide nonvolatile and/or volatile storage of machine-readableinstructions, data structures, program modules, and/or other data forcomputer system 600. In one example, software 620 may reside, completelyor partially, within machine-readable medium 628. In another example,software 620 may reside, completely or partially, within processor 604.

Computer system 600 may also include an input device 632. In oneexample, a user of computer system 600 may enter commands and/or otherinformation into computer system 600 via input device 632. Examples ofan input device 632 include, but are not limited to, an alpha-numericinput device (e.g., a keyboard), a pointing device, a joystick, agamepad, an audio input device (e.g., a microphone, a voice responsesystem, etc.), a cursor control device (e.g., a mouse), a touchpad, anoptical scanner, a video capture device (e.g., a still camera, a videocamera), a touchscreen, and any combinations thereof. Input device 632may be interfaced to bus 612 via any of a variety of interfaces (notshown) including, but not limited to, a serial interface, a parallelinterface, a game port, a USB interface, a FIREWIRE interface, a directinterface to bus 612, and any combinations thereof. Input device 632 mayinclude a touch screen interface that may be a part of or separate fromdisplay 636, discussed further below. Input device 632 may be utilizedas a user selection device for selecting one or more graphicalrepresentations in a graphical interface as described above.

A user may also input commands and/or other information to computersystem 600 via storage device 624 (e.g., a removable disk drive, a flashdrive, etc.) and/or network interface device 640. A network interfacedevice, such as network interface device 640, may be utilized forconnecting computer system 600 to one or more of a variety of networks,such as network 644, and one or more remote devices 648 connectedthereto. Examples of a network interface device include, but are notlimited to, a network interface card (e.g., a mobile network interfacecard, a LAN card), a modem, and any combination thereof. Examples of anetwork include, but are not limited to, a wide area network (e.g., theInternet, an enterprise network), a local area network (e.g., a networkassociated with an office, a building, a campus or other relativelysmall geographic space), a telephone network, a data network associatedwith a telephone/voice provider (e.g., a mobile communications providerdata and/or voice network), a direct connection between two computingdevices, and any combinations thereof. A network, such as network 644,may employ a wired and/or a wireless mode of communication. In general,any network topology may be used. Information (e.g., data, software 620,etc.) may be communicated to and/or from computer system 600 via networkinterface device 640.

Computer system 600 may further include a video display adapter 652 forcommunicating a displayable image to a display device, such as displaydevice 636. Examples of a display device include, but are not limitedto, a liquid crystal display (LCD), a cathode ray tube (CRT), a plasmadisplay, a light emitting diode (LED) display, and any combinationsthereof. Display adapter 652 and display device 636 may be utilized incombination with processor 604 to provide graphical representations ofaspects of the present disclosure. In addition to a display device,computer system 600 may include one or more other peripheral outputdevices including, but not limited to, an audio speaker, a printer, andany combinations thereof. Such peripheral output devices may beconnected to bus 612 via a peripheral interface 656. Examples of aperipheral interface include, but are not limited to, a serial port, aUSB connection, a FIREWIRE connection, a parallel connection, and anycombinations thereof.

The foregoing has been a detailed description of illustrativeembodiments of the invention. Various modifications and additions can bemade without departing from the spirit and scope of this invention.Features of each of the various embodiments described above may becombined with features of other described embodiments as appropriate inorder to provide a multiplicity of feature combinations in associatednew embodiments. Furthermore, while the foregoing describes a number ofseparate embodiments, what has been described herein is merelyillustrative of the application of the principles of the presentinvention. Additionally, although particular methods herein may beillustrated and/or described as being performed in a specific order, theordering is highly variable within ordinary skill to achieve methods,systems, and software according to the present disclosure. Accordingly,this description is meant to be taken only by way of example, and not tootherwise limit the scope of this invention.

Exemplary embodiments have been disclosed above and illustrated in theaccompanying drawings. It will be understood by those skilled in the artthat various changes, omissions and additions may be made to that whichis specifically disclosed herein without departing from the spirit andscope of the present invention.

What is claimed is:
 1. A system for generating lifestyle changerecommendations based on biological extractions, the system comprising acomputing device, the computing device designed and configured for:receiving a biological extraction pertaining to a user; generating,using a first machine-learning process, a plurality of lifestyleintervention combinations as a function of the biological extraction,wherein generating the plurality of lifestyle intervention combinationsfurther comprises: training a first machine-learning model using a firsttraining data set and the first machine-learning process, wherein thefirst training data set includes entries correlating biologicalextraction data with lifestyle intervention combinations; and utilizingthe first machine-learning model to output the plurality of lifestyleintervention combinations using the biological extraction as an input;assigning, using a second machine-learning process, to each lifestyleintervention combination of the plurality of lifestyle interventioncombinations, a projected degree of user adherence to each lifestyleintervention combination indicating an estimated degree of adherence toeach lifestyle intervention combination by the user, wherein assigningthe projected degree of user adherence further comprises: training asecond machine-learning model using a second training data set and thesecond machine-learning process, wherein the second training data setincludes entries correlating lifestyle intervention combinations withadherence data; and utilizing the second machine-learning model tooutput the projected degree of user adherence using the lifestyleintervention combination as an input; and selecting, from the pluralityof lifestyle intervention combinations, a lifestyle interventioncombination as a function of the projected degree of user adherence ofthe selected lifestyle intervention combination.
 2. The system of claim1, wherein generating the plurality of lifestyle interventioncombinations further comprises: receiving a description of at least acurrent disease state of the user; and generating the plurality oflifestyle intervention combinations as a function of the current diseasestate.
 3. The system of claim 1, wherein generating the plurality oflifestyle intervention combinations further comprises: identifying anegative lifestyle behavior of the user; and generating a plurality oflifestyle intervention combinations, wherein each lifestyle interventioncombination alleviates the negative lifestyle behavior.
 4. The system ofclaim 3, wherein identifying the negative lifestyle behavior furthercomprises: receiving a training set correlating biological extractionsto negative lifestyle behaviors; generating negative behavior identifiermodel using a supervised machine-learning algorithm and the trainingset; producing a negative lifestyle behavior output from the negativebehavior identifier model using the biological extraction; andidentifying the negative lifestyle behavior as a function of thenegative behavior output.
 5. The system of claim 1, wherein assigningthe projected degree of user adherence further comprises: providing auser inclination enumeration; determining, using a classifier, adistance from the user inclination enumeration to each lifestyleintervention combination of the plurality of lifestyle interventioncombinations; and assigning the projected degree of user adherence usingthe distance.
 6. The system of claim 5, wherein providing the userinclination enumeration further comprises: generating a defaultenumeration; displaying the default enumeration to the user; receiving auser command modifying the default enumeration; and deriving the userinclination enumeration using the default enumeration and the usercommand.
 7. The system of claim 6, wherein generating the defaultenumeration further comprises: receiving a training set correlating acohort of individual information to individual user inclinationenumerations; generating a set of user data regarding the user; andderiving the default enumeration from the training set as a function ofthe set of user data using a classification process.
 8. The system ofclaim 1, wherein assigning the projected degree of user adherencefurther comprises: receiving a training set correlating individual userinclination enumerations and lifestyle intervention combinations withadherence data; generating, using the training set, an adherence scorefor each lifestyle intervention combination of the plurality oflifestyle combinations as a function of a user inclination enumerationassociated with the user; and assigning the projected degree of useradherence as a function of the adherence score.
 9. The system of claim1, wherein assigning the projected degree of user adherence furthercomprises: receiving a training set correlating user data and lifestyleintervention combinations with adherence data; and generating, using thetraining set, an adherence score for each lifestyle interventioncombination of the plurality of lifestyle combinations as a function ofuser data associated with the user: and assigning the projected degreeof user adherence as a function of the adherence score.
 10. The systemof claim 1, wherein the computing device is further configured to:receive a user belief proscription; identify a lifestyle interventioncombination of the plurality of lifestyle intervention combinations thatconflicts with the user belief proscription; and eliminate theidentified lifestyle intervention combination from the plurality oflifestyle intervention combinations.
 11. A method of generatinglifestyle change recommendations based on biological extractions, themethod comprising: receiving, by a computing device, a biologicalextraction pertaining to a user; generating, by the computing device,and using a first machine-learning process, a plurality of lifestyleintervention combinations as a function of the biological extraction,wherein generating the plurality of lifestyle intervention combinationsfurther comprises: training a first machine-learning model using a firsttraining data set and the first machine-learning process, wherein thefirst training data set includes entries correlating biologicalextraction data with lifestyle intervention combinations; and utilizingthe first machine-learning model to output the plurality of lifestyleintervention combinations using the biological extraction as an input;assigning, by the computing device, and using a second machine-learningprocess, to each lifestyle intervention combination of the plurality oflifestyle intervention combinations, a projected degree of useradherence to each lifestyle intervention combination indicating anestimated degree of adherence to each lifestyle intervention combinationby the user, wherein assigning the projected degree of user adherencefurther comprises: training a second machine-learning model using asecond training data set and the second machine-learning process,wherein the second training data set includes entries correlatinglifestyle intervention combinations with adherence data; and utilizingthe second machine-learning model to output the projected degree of useradherence using the lifestyle intervention combination as an input; andselecting, by the computing device, from the plurality of lifestyleintervention combinations, a lifestyle intervention combination as afunction of the projected degree of user adherence to the selectedlifestyle intervention combination.
 12. The method of claim 11, whereingenerating the plurality of lifestyle intervention combinations furthercomprises: receiving a description of at least a current disease stateof the user; and generating the plurality of lifestyle interventioncombinations as a function of the current disease state.
 13. The methodof claim 11, wherein generating the plurality of lifestyle interventioncombinations further comprises: identifying a negative lifestylebehavior of the user; and generating a plurality of lifestyleintervention combinations, wherein each lifestyle interventioncombination alleviates the negative lifestyle behavior.
 14. The methodof claim 13, wherein identifying the negative lifestyle behavior furthercomprises: receiving a training set correlating biological extractionsto negative lifestyle behaviors; generating negative behavior identifiermodel using a supervised machine-learning algorithm and the trainingset; producing a negative lifestyle behavior output from the negativebehavior identifier model using the biological extraction; andidentifying the negative lifestyle behavior as a function of thenegative behavior output.
 15. The method of claim 11, wherein assigningthe projected degree of user adherence further comprises: providing auser inclination enumeration; determining, using a classifier, adistance from the user inclination enumeration to each lifestyleintervention combination of the plurality of lifestyle interventioncombinations; and assigning the projected degree of user adherence usingthe distance.
 16. The method of claim 15, wherein providing the userinclination enumeration further comprises: generating a defaultenumeration; displaying the default enumeration to the user; receiving auser command modifying the default enumeration; and deriving the userinclination enumeration using the default enumeration and the usercommand.
 17. The method of claim 16, wherein generating the defaultenumeration further comprises: receiving a training set correlating acohort of individual information to individual user inclinationenumerations; retrieving a set of user data regarding the user; andderiving the default enumeration from the training set as a function ofthe set of user data using a classification process.
 18. The method ofclaim 11 further comprising: receiving a training set correlatingindividual user inclination enumerations and lifestyle interventioncombinations with adherence data; generating, using the training set, anadherence score for each lifestyle intervention combination of theplurality of lifestyle combinations as a function of the userinclination enumeration; and assigning the projected degree of useradherence as a function of the adherence score.
 19. The method of claim11, wherein assigning the projected degree of user adherence furthercomprises: receiving a training set correlating user data and lifestyleintervention combinations with adherence data; and generating, using thetraining set, an adherence score for each lifestyle interventioncombination of the plurality of lifestyle combinations as a function ofuser data of the user; and assigning the projected degree of useradherence as a function of the adherence score.
 20. The method of claim11 further comprising: receiving a user belief proscription; identifyinga lifestyle intervention combination of the plurality of lifestyleintervention combinations that conflicts with the user beliefproscription; and eliminating the identified lifestyle interventioncombination from the plurality of lifestyle intervention combinations.